diff --git a/dev/.documenter-siteinfo.json b/dev/.documenter-siteinfo.json index 28f6aaa43..31d44cf5a 100644 --- a/dev/.documenter-siteinfo.json +++ b/dev/.documenter-siteinfo.json @@ -1 +1 @@ -{"documenter":{"julia_version":"1.10.0","generation_timestamp":"2024-01-09T15:50:26","documenter_version":"1.2.1"}} \ No newline at end of file +{"documenter":{"julia_version":"1.10.0","generation_timestamp":"2024-02-12T08:20:58","documenter_version":"1.2.1"}} \ No newline at end of file diff --git a/dev/custom/custom-addons/index.html b/dev/custom/custom-addons/index.html index ba09a1fbf..703b0bf7f 100644 --- a/dev/custom/custom-addons/index.html +++ b/dev/custom/custom-addons/index.html @@ -1,5 +1,5 @@ -Custom addons · ReactiveMP.jl
GitHub

Custom Addons

Standard message passing schemes only pass along distributions to other nodes. However, for more advanced usage, there might be a need for passing along additional information in messages and/or marginals. One can for example think of passing along the scaling of the distribution or some information that specifies how the message or marginal was computed, i.e. which messages were used for its computation and which node was preceding it. Another use cases is saving extra debugging information inside messages themselves, e.g. what arguments have been used to compute a message.

Addons provide a solution here. Basically, addons are structures that contain extra information that are passed along the graph with messages and marginals in a tuple. These addons can be extracted using the getaddons(message/marginal) function. Its usage and operations can differ significantly for each application, yet below gives a concise overview on how to implement them on your own.

Example

Suppose that we wish to create an addon that counts the number of computations that preceded some message or marginal. This addon can be created by adding the file src/addons/count.jl and by including it in the ReactiveMP.jl file.

Step 1: Creating the addon structure

Let's start by defining our new addon structure. This might seem daunting, but basically only requires us to specify the information that we would like to collect. Just make sure that it is specified as a subtype of AbstractAddon. In our example this becomes:

struct AddonCount{T} <: AbstractAddon
+Custom addons · ReactiveMP.jl
GitHub
Custom Addons
Standard message passing schemes only pass along distributions to other nodes. However, for more advanced usage, there might be a need for passing along additional information in messages and/or marginals. One can for example think of passing along the scaling of the distribution or some information that specifies how the message or marginal was computed, i.e. which messages were used for its computation and which node was preceding it. Another use cases is saving extra debugging information inside messages themselves, e.g. what arguments have been used to compute a message.
Addons provide a solution here. Basically, addons are structures that contain extra information that are passed along the graph with messages and marginals in a tuple. These addons can be extracted using the getaddons(message/marginal) function. Its usage and operations can differ significantly for each application, yet below gives a concise overview on how to implement them on your own.
Example
Suppose that we wish to create an addon that counts the number of computations that preceded some message or marginal. This addon can be created by adding the file src/addons/count.jl and by including it in the ReactiveMP.jl file.
Step 1: Creating the addon structure
Let's start by defining our new addon structure. This might seem daunting, but basically only requires us to specify the information that we would like to collect. Just make sure that it is specified as a subtype of AbstractAddon. In our example this becomes:
struct AddonCount{T} <: AbstractAddon
     count :: T
 end
You can add additional fields or functions for improved handling, such as get_count() or show() functions.
Step 2: Compute addon value after computing a message
As a second step we need to specify how the addon behaves when a new message is computed in a factor node.  For this purpose we need to implement a specialized version of the message_mapping_addon() function. This function accepts the mapping variables of the factor node and updates the addons by extending the tuple.
In our example we could write
# This specification assumes that the default value for addon is `AddonCount(nothing)`
 function message_mapping_addon(::AddonCount{Nothing}, mapping, messages, marginals, result, addons)
@@ -21,4 +21,4 @@
 end
 
Step 3: Computing products
The goal is to update the AddonCount structure when we multiply 2 messages. As a result, we need to write a function that allows us to define this behaviour. This function is called multiply_addons and accepts 5 arguments. In our example this becomes
function multiply_addons(left_addon::AddonCount, right_addon::AddonCount, new_dist, left_dist, right_dist)
     return AddonCount(left_addon.count + right_addon.count + 1)
-end
here we add the number of operations from the addons that are being multiplied and we add one (for the current operation). we are aware that this is likely not valid for iterative message passing schemes, but it still serves as a nice example. the left_addon and right_addon argument specify the addoncount objects that are being multiplied. corresponding to these addons, there are the distributions left_dist and right_dist, which might contain information for computing the product. the new distribution new_dist ∝ left_dist * right_dist is also passed along for potentially reusing the result of earlier computations.
More information
For more advanced information check the implementation of the log-scale or memory addons.

+end

here we add the number of operations from the addons that are being multiplied and we add one (for the current operation). we are aware that this is likely not valid for iterative message passing schemes, but it still serves as a nice example. the left_addon and right_addon argument specify the addoncount objects that are being multiplied. corresponding to these addons, there are the distributions left_dist and right_dist, which might contain information for computing the product. the new distribution new_dist ∝ left_dist * right_dist is also passed along for potentially reusing the result of earlier computations.

More information

For more advanced information check the implementation of the log-scale or memory addons.

diff --git a/dev/custom/custom-functional-form/index.html b/dev/custom/custom-functional-form/index.html index c811d6f53..248b39ef1 100644 --- a/dev/custom/custom-functional-form/index.html +++ b/dev/custom/custom-functional-form/index.html @@ -1,5 +1,5 @@ -Custom functional form · ReactiveMP.jl
GitHub

Custom Functional Form Specification

In a nutshell, functional form constraints defines a function that approximates the product of colliding messages and computes posterior marginal that can be used later on during the inference procedure. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. For example, if we refer to our CustomFunctionalForm as to f we can see the whole functional form constraints pipeline as:

\[q(x) = f\left(\frac{\overrightarrow{\mu}(x)\overleftarrow{\mu}(x)}{\int \overrightarrow{\mu}(x)\overleftarrow{\mu}(x) \mathrm{d}x}\right)\]

Interface

ReactiveMP.jl, however, uses some extra utility functions to define functional form constraint behaviour. Here we briefly describe all utility function. If you are only interested in the concrete example, you may directly head to the Custom Functional Form example at the end of this section.

Abstract super type

ReactiveMP.AbstractFormConstraint — Type
AbstractFormConstraint

Every functional form constraint is a subtype of AbstractFormConstraint abstract type.

Note: this is not strictly necessary, but it makes automatic dispatch easier and compatible with the CompositeFormConstraint.

See also: CompositeFormConstraint

source
ReactiveMP.CompositeFormConstraint — Type
CompositeFormConstraint

Creates a composite form constraint that applies form constraints in order. The composed form constraints must be compatible and have the exact same form_check_strategy. Any functional form constraint that defines is_point_mass_form_constraint() = true may be used only as the last element of the composition.

source

Form check strategy

Every custom functional form must implement a new method for the default_form_check_strategy function that returns either FormConstraintCheckEach or FormConstraintCheckLast.

  • FormConstraintCheckLast: q(x) = f(μ1(x) * μ2(x) * μ3(x))
  • FormConstraintCheckEach: q(x) = f(f(μ1(x) * μ2(x)) * μ3(x))
ReactiveMP.FormConstraintCheckEach — Type
FormConstraintCheckEach

This form constraint check strategy checks functional form of the messages product after each product in an equality chain. Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed after each subsequent prod function.

See also: FormConstraintCheckLast, default_form_check_strategy, constrain_form

source
ReactiveMP.FormConstraintCheckLast — Type
FormConstraintCheckEach

This form constraint check strategy checks functional form of the last messages product in the equality chain. Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed only once after all subsequenct prod functions have been executed.

See also: FormConstraintCheckLast, default_form_check_strategy, constrain_form

source

Prod constraint

Every custom functional form must implement a new method for the default_prod_constraint function that returns a proper prod_constraint object.

ReactiveMP.default_prod_constraint — Function
default_prod_constraint(form_constraint)

Returns a default prod constraint needed to apply a given form_constraint. For most form constraints this function returns ProdGeneric.

source

Constrain form, a.k.a f

The main function that a custom functional form must implement, which we referred to as f in the beginning of this section, is the constrain_form function.

Is point mass form constraint (optional)

Every custom functional form may implement a new method for the is_point_mass_form_constraint function that returns either true or false. This is an utility function that simplifes computation of the Bethe Free Energy and is not strictly necessary.

Compatibility with @constraints macro (optional)

To make custom functional form constraint compatible with the @constraints macro, it must implement a new method for the make_form_constraint function.

ReactiveMP.make_form_constraint — Function
make_form_constraint(::Type, args...; kwargs...)

Creates form constraint object based on passed type with given args and kwargs. Used to simplify form constraint specification.

As an example:

make_form_constraint(PointMass)

creates an instance of PointMassFormConstraint and 

make_form_constraint(SampleList, 5000, LeftProposal())

should create an instance of SampleListFormConstraint.

See also: AbstractFormConstraint

source

Custom Functional Form Example

In this demo we show how to build a custom functional form constraint that is compatible with the ReactiveMP.jl inference backend. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. We show a relatively simple use-case, which might not be very useful in practice, but serves as a simple step-by-step guide. Assume that we want a specific posterior marginal of some random variable in our model to have a specific Gaussian parametrisation, for example mean-precision. We can use built-in NormalMeanPrecision distribution, but we still need to define our custom functional form constraint:

using ReactiveMP, BayesBase
+Custom functional form · ReactiveMP.jl
GitHub
Custom Functional Form Specification
In a nutshell, functional form constraints defines a function that approximates the product of colliding messages and computes posterior marginal that can be used later on during the inference procedure. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. For example, if we refer to our CustomFunctionalForm as to f we can see the whole functional form constraints pipeline as:
\[q(x) = f\left(\frac{\overrightarrow{\mu}(x)\overleftarrow{\mu}(x)}{\int \overrightarrow{\mu}(x)\overleftarrow{\mu}(x) \mathrm{d}x}\right)\]
Interface
ReactiveMP.jl, however, uses some extra utility functions to define functional form constraint behaviour. Here we briefly describe all utility function. If you are only interested in the concrete example, you may directly head to the Custom Functional Form example at the end of this section.
Abstract super type
ReactiveMP.AbstractFormConstraint — Type
AbstractFormConstraint
Every functional form constraint is a subtype of AbstractFormConstraint abstract type.
Note: this is not strictly necessary, but it makes automatic dispatch easier and compatible with the CompositeFormConstraint.
See also: CompositeFormConstraint
source
ReactiveMP.CompositeFormConstraint — Type
CompositeFormConstraint
Creates a composite form constraint that applies form constraints in order. The composed form constraints must be compatible and have the exact same form_check_strategy.  Any functional form constraint that defines is_point_mass_form_constraint() = true may be used only as the last element of the composition.
source
Form check strategy
Every custom functional form must implement a new method for the default_form_check_strategy function that returns either FormConstraintCheckEach or FormConstraintCheckLast.
  • FormConstraintCheckLast: q(x) = f(μ1(x) * μ2(x) * μ3(x))
  • FormConstraintCheckEach: q(x) = f(f(μ1(x) * μ2(x)) * μ3(x))
ReactiveMP.FormConstraintCheckEach — Type
FormConstraintCheckEach
This form constraint check strategy checks functional form of the messages product after each product in an equality chain.  Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed after each subsequent prod function.
See also: FormConstraintCheckLast, default_form_check_strategy, constrain_form
source
ReactiveMP.FormConstraintCheckLast — Type
FormConstraintCheckEach
This form constraint check strategy checks functional form of the last messages product in the equality chain.  Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed only once after all subsequenct prod functions have been executed.
See also: FormConstraintCheckLast, default_form_check_strategy, constrain_form
source
Prod constraint
Every custom functional form must implement a new method for the default_prod_constraint function that returns a proper prod_constraint object.
ReactiveMP.default_prod_constraint — Function
default_prod_constraint(form_constraint)
Returns a default prod constraint needed to apply a given form_constraint. For most form constraints this function returns ProdGeneric.
source
Constrain form, a.k.a f
The main function that a custom functional form must implement, which we referred to as f in the beginning of this section, is the constrain_form function.
Is point mass form constraint (optional)
Every custom functional form may implement a new method for the is_point_mass_form_constraint function that returns either true or false. This is an utility function that simplifes computation of the Bethe Free Energy and is not strictly necessary.
Compatibility with @constraints macro (optional)
To make custom functional form constraint compatible with the @constraints macro, it must implement a new method for the make_form_constraint function.
ReactiveMP.make_form_constraint — Function
make_form_constraint(::Type, args...; kwargs...)
Creates form constraint object based on passed type with given args and kwargs. Used to simplify form constraint specification.
As an example:
make_form_constraint(PointMass)
creates an instance of PointMassFormConstraint and 
make_form_constraint(SampleList, 5000, LeftProposal())
should create an instance of SampleListFormConstraint.
See also: AbstractFormConstraint
source
Custom Functional Form Example
In this demo we show how to build a custom functional form constraint that is compatible with the ReactiveMP.jl inference backend. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. We show a relatively simple use-case, which might not be very useful in practice, but serves as a simple step-by-step guide. Assume that we want a specific posterior marginal of some random variable in our model to have a specific Gaussian parametrisation, for example mean-precision. We can use built-in NormalMeanPrecision distribution, but we still need to define our custom functional form constraint:
using ReactiveMP, BayesBase
 
 # First we define our functional form structure with no fields
 struct MeanPrecisionFormConstraint <: AbstractFormConstraint end
Next we define the behaviour of our functional form constraint:
ReactiveMP.is_point_mass_form_constraint(::MeanPrecisionFormConstraint) = false
@@ -18,4 +18,4 @@
     # ProductOf is the special case, read about this type more in the corresponding documentation section
     # of the `BayesBase` package
     # ...
-end

+end
diff --git a/dev/extra/contributing/index.html b/dev/extra/contributing/index.html index 0fabacb09..2c7479f0c 100644 --- a/dev/extra/contributing/index.html +++ b/dev/extra/contributing/index.html @@ -1,2 +1,2 @@ -Contributing · ReactiveMP.jl
GitHub

Contribution guidelines

We welcome all possible contributors. This page details the some of the guidelines that should be followed when contributing to this package.

Reporting bugs

We track bugs using GitHub issues. We encourage you to write complete, specific, reproducible bug reports. Mention the versions of Julia and ReactiveMP for which you observe unexpected behavior. Please provide a concise description of the problem and complement it with code snippets, test cases, screenshots, tracebacks or any other information that you consider relevant. This will help us to replicate the problem and narrow the search space for solutions.

Suggesting features

We welcome new feature proposals. However, before submitting a feature request, consider a few things:

  • Does the feature require changes in the core ReactiveMP.jl code? If it doesn't (for example, you would like to add a factor node for a particular application), you can add local extensions in your script/notebook or consider making a separate repository for your extensions.
  • If you would like to add an implementation of a feature that changes a lot in the core ReactiveMP.jl code, please open an issue on GitHub and describe your proposal first. This will allow us to discuss your proposal with you before you invest your time in implementing something that may be difficult to merge later on.

Contributing code

Installing ReactiveMP

We suggest that you use the dev command from the new Julia package manager to install ReactiveMP.jl for development purposes. To work on your fork of ReactiveMP.jl, use your fork's URL address in the dev command, for example:

] dev git@github.com:your_username/ReactiveMP.jl.git

The dev command clones ReactiveMP.jl to ~/.julia/dev/ReactiveMP. All local changes to ReactiveMP code will be reflected in imported code.

Note

It is also might be useful to install Revise.jl package as it allows you to modify code and use the changes without restarting Julia.

Committing code

We use the standard GitHub Flow workflow where all contributions are added through pull requests. In order to contribute, first fork the repository, then commit your contributions to your fork, and then create a pull request on the master branch of the ReactiveMP.jl repository.

Before opening a pull request, please make sure that all tests pass without failing! All demos (can be found in /demo/ directory) and benchmarks (can be found in /benchmark/ directory) have to run without errors as well.

Style conventions

Note

ReactiveMP.jl repository contains scripts to automatically format code according to our guidelines. Use make format command to fix code style. This command overwrites files.

We use default Julia style guide. We list here a few important points and our modifications to the Julia style guide:

  • Use 4 spaces for indentation
  • Type names use UpperCamelCase. For example: AbstractFactorNode, RandomVariable, etc..
  • Function names are lowercase with underscores, when necessary. For example: activate!, randomvar, as_variable, etc..
  • Variable names and function arguments use snake_case
  • The name of a method that modifies its argument(s) must end in !

Unit tests

We use the test-driven development (TDD) methodology for ReactiveMP.jl development. The test coverage should be as complete as possible. Please make sure that you write tests for each piece of code that you want to add.

All unit tests are located in the /test/ directory. The /test/ directory follows the structure of the /src/ directory. Each test file should have following filename format: test_*.jl. Some tests are also present in jldoctest docs annotations directly in the source code. See Julia's documentation about doctests.

The tests can be evaluated by running following command in the Julia REPL:

] test ReactiveMP

In addition tests can be evaluated by running following command in the ReactiveMP root directory:

make test

Fixes to external libraries

If a bug has been discovered in an external dependencies of the ReactiveMP.jl it is the best to open an issue directly in the dependency's github repository. You use can use the fixes.jl file for hot-fixes before a new release of the broken dependecy is available.

Makefile

ReactiveMP.jl uses Makefile for most common operations:

  • make help: Shows help snippet
  • make test: Run tests, supports extra arguments
    • make test test_args="distributions:normal_mean_variance" would run tests only from distributions/test_normal_mean_variance.jl
    • make test test_args="distributions:normal_mean_variance models:lgssm" would run tests both from distributions/test_normal_mean_variance.jl and models/test_lgssm.jl
  • make docs: Compile documentation
  • make benchmark: Run simple benchmark
  • make lint: Check codestyle
  • make format: Check and fix codestyle
+Contributing · ReactiveMP.jl
GitHub

Contribution guidelines

We welcome all possible contributors. This page details the some of the guidelines that should be followed when contributing to this package.

Reporting bugs

We track bugs using GitHub issues. We encourage you to write complete, specific, reproducible bug reports. Mention the versions of Julia and ReactiveMP for which you observe unexpected behavior. Please provide a concise description of the problem and complement it with code snippets, test cases, screenshots, tracebacks or any other information that you consider relevant. This will help us to replicate the problem and narrow the search space for solutions.

Suggesting features

We welcome new feature proposals. However, before submitting a feature request, consider a few things:

  • Does the feature require changes in the core ReactiveMP.jl code? If it doesn't (for example, you would like to add a factor node for a particular application), you can add local extensions in your script/notebook or consider making a separate repository for your extensions.
  • If you would like to add an implementation of a feature that changes a lot in the core ReactiveMP.jl code, please open an issue on GitHub and describe your proposal first. This will allow us to discuss your proposal with you before you invest your time in implementing something that may be difficult to merge later on.

Contributing code

Installing ReactiveMP

We suggest that you use the dev command from the new Julia package manager to install ReactiveMP.jl for development purposes. To work on your fork of ReactiveMP.jl, use your fork's URL address in the dev command, for example:

] dev git@github.com:your_username/ReactiveMP.jl.git

The dev command clones ReactiveMP.jl to ~/.julia/dev/ReactiveMP. All local changes to ReactiveMP code will be reflected in imported code.

Note

It is also might be useful to install Revise.jl package as it allows you to modify code and use the changes without restarting Julia.

Committing code

We use the standard GitHub Flow workflow where all contributions are added through pull requests. In order to contribute, first fork the repository, then commit your contributions to your fork, and then create a pull request on the master branch of the ReactiveMP.jl repository.

Before opening a pull request, please make sure that all tests pass without failing! All demos (can be found in /demo/ directory) and benchmarks (can be found in /benchmark/ directory) have to run without errors as well.

Style conventions

Note

ReactiveMP.jl repository contains scripts to automatically format code according to our guidelines. Use make format command to fix code style. This command overwrites files.

We use default Julia style guide. We list here a few important points and our modifications to the Julia style guide:

  • Use 4 spaces for indentation
  • Type names use UpperCamelCase. For example: AbstractFactorNode, RandomVariable, etc..
  • Function names are lowercase with underscores, when necessary. For example: activate!, randomvar, as_variable, etc..
  • Variable names and function arguments use snake_case
  • The name of a method that modifies its argument(s) must end in !

Unit tests

We use the test-driven development (TDD) methodology for ReactiveMP.jl development. The test coverage should be as complete as possible. Please make sure that you write tests for each piece of code that you want to add.

All unit tests are located in the /test/ directory. The /test/ directory follows the structure of the /src/ directory. Each test file should have following filename format: test_*.jl. Some tests are also present in jldoctest docs annotations directly in the source code. See Julia's documentation about doctests.

The tests can be evaluated by running following command in the Julia REPL:

] test ReactiveMP

In addition tests can be evaluated by running following command in the ReactiveMP root directory:

make test

Fixes to external libraries

If a bug has been discovered in an external dependencies of the ReactiveMP.jl it is the best to open an issue directly in the dependency's github repository. You use can use the fixes.jl file for hot-fixes before a new release of the broken dependecy is available.

Makefile

ReactiveMP.jl uses Makefile for most common operations:

  • make help: Shows help snippet
  • make test: Run tests, supports extra arguments
    • make test test_args="distributions:normal_mean_variance" would run tests only from distributions/test_normal_mean_variance.jl
    • make test test_args="distributions:normal_mean_variance models:lgssm" would run tests both from distributions/test_normal_mean_variance.jl and models/test_lgssm.jl
  • make docs: Compile documentation
  • make benchmark: Run simple benchmark
  • make lint: Check codestyle
  • make format: Check and fix codestyle
diff --git a/dev/extra/extensions/index.html b/dev/extra/extensions/index.html index 98a275f6d..6374a7979 100644 --- a/dev/extra/extensions/index.html +++ b/dev/extra/extensions/index.html @@ -1,2 +1,2 @@ -Extensions and interaction with the Julia ecosystem · ReactiveMP.jl
GitHub

Extensions and interaction with the Julia ecosystem

ReactiveMP.jl exports extra functionality if other Julia packages are loaded in the same environment.

Optimisers.jl

The Optimizers.jl package defines many standard gradient-based optimisation rules, and tools for applying them to deeply nested models. The optimizers defined in the Optimziers.jl are compatible with the CVI approximation method.

DiffResults.jl (loaded automatically with the ForwardDiff.jl)

The DiffResults.jl provides the DiffResult type, which can be passed to in-place differentiation methods instead of an output buffer. If loaded in the current Julia session enables faster derivatives with the ForwardDiffGrad option in the CVI approximation method (in the Gaussian case).

+Extensions and interaction with the Julia ecosystem · ReactiveMP.jl
GitHub

Extensions and interaction with the Julia ecosystem

ReactiveMP.jl exports extra functionality if other Julia packages are loaded in the same environment.

Optimisers.jl

The Optimizers.jl package defines many standard gradient-based optimisation rules, and tools for applying them to deeply nested models. The optimizers defined in the Optimziers.jl are compatible with the CVI approximation method.

DiffResults.jl (loaded automatically with the ForwardDiff.jl)

The DiffResults.jl provides the DiffResult type, which can be passed to in-place differentiation methods instead of an output buffer. If loaded in the current Julia session enables faster derivatives with the ForwardDiffGrad option in the CVI approximation method (in the Gaussian case).

diff --git a/dev/index.html b/dev/index.html index c2957c3aa..e2d9c6679 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -Introduction · ReactiveMP.jl
GitHub

ReactiveMP.jl

Julia package for reactive message passing Bayesian inference engine on a factor graph.

Note

This package exports only an inference engine, for the full ecosystem with convenient model and constraints specification we refer user to the RxInfer.jl package and its documentation.

Examples and tutorials

Tutorials and examples are available in the RxInfer documentation.

Table of Contents

Index

+Introduction · ReactiveMP.jl
GitHub

ReactiveMP.jl

Julia package for reactive message passing Bayesian inference engine on a factor graph.

Note

This package exports only an inference engine, for the full ecosystem with convenient model and constraints specification we refer user to the RxInfer.jl package and its documentation.

Examples and tutorials

Tutorials and examples are available in the RxInfer documentation.

Table of Contents

Index

diff --git a/dev/lib/algebra/common/index.html b/dev/lib/algebra/common/index.html index b2f53c5a1..938da075e 100644 --- a/dev/lib/algebra/common/index.html +++ b/dev/lib/algebra/common/index.html @@ -1,2 +1,2 @@ -Algebra utils · ReactiveMP.jl
GitHub

Algebra common utilities

diageye

ReactiveMP.diageye — Function
diageye(::Type{T}, n::Int)

An alias for the Matrix{T}(I, n, n). Returns a matrix of size n x n with ones (of type T) on the diagonal and zeros everywhere else.

source
diageye(n::Int)

An alias for the Matrix{Float64}(I, n, n). Returns a matrix of size n x n with ones (of type Float64) on the diagonal and zeros everywhere else.

source
+Algebra utils · ReactiveMP.jl
GitHub

Algebra common utilities

diageye

ReactiveMP.diageye — Function
diageye(::Type{T}, n::Int)

An alias for the Matrix{T}(I, n, n). Returns a matrix of size n x n with ones (of type T) on the diagonal and zeros everywhere else.

source
diageye(n::Int)

An alias for the Matrix{Float64}(I, n, n). Returns a matrix of size n x n with ones (of type Float64) on the diagonal and zeros everywhere else.

source
diff --git a/dev/lib/helpers/index.html b/dev/lib/helpers/index.html index d5b12df7d..06eaa3fa5 100644 --- a/dev/lib/helpers/index.html +++ b/dev/lib/helpers/index.html @@ -1,5 +1,5 @@ -Helper utils · ReactiveMP.jl
GitHub

Helper utilities

ReactiveMP implements various structures/functions/methods as "helper" structures that might be useful in various contexts.

SkipIndexIterator

ReactiveMP.SkipIndexIterator — Type
SkipIndexIterator

A special type of iterator that simply iterates over internal iterator, but skips index skip.

Arguments

  • iterator: internal iterator
  • skip: index to skip (integer)

See also: skipindex

source
ReactiveMP.skipindex — Function
skipindex(iterator, skip)

Creation operator for SkipIndexIterator.

julia> s = ReactiveMP.skipindex(1:3, 2)
+Helper utils · ReactiveMP.jl
GitHub
Helper utilities
ReactiveMP implements various structures/functions/methods as "helper" structures that might be useful in various contexts.
SkipIndexIterator
ReactiveMP.SkipIndexIterator — Type
SkipIndexIterator
A special type of iterator that simply iterates over internal iterator, but skips index skip.
Arguments
  • iterator: internal iterator
  • skip: index to skip (integer)
See also: skipindex
source
ReactiveMP.skipindex — Function
skipindex(iterator, skip)
Creation operator for SkipIndexIterator.
julia> s = ReactiveMP.skipindex(1:3, 2)
 2-element ReactiveMP.SkipIndexIterator{Int64, UnitRange{Int64}}:
  1
  3
@@ -7,4 +7,4 @@
 julia> collect(s)
 2-element Vector{Int64}:
  1
- 3
See also: SkipIndexIterator
source
FunctionalIndex
ReactiveMP.FunctionalIndex — Type
FunctionalIndex
A special type of an index that represents a function that can be used only in pair with a collection.  An example of a FunctionalIndex can be firstindex or lastindex, but more complex use cases are possible too,  e.g. firstindex + 1. Important part of the implementation is that the resulting structure is isbitstype(...) = true, that allows to store it in parametric type as valtype.
One use case for this structure is to dispatch on and to replace begin or end (or more complex use cases, e.g. begin + 1) markers in constraints specification language.
source

+ 3

See also: SkipIndexIterator

source

FunctionalIndex

ReactiveMP.FunctionalIndex — Type
FunctionalIndex

A special type of an index that represents a function that can be used only in pair with a collection. An example of a FunctionalIndex can be firstindex or lastindex, but more complex use cases are possible too, e.g. firstindex + 1. Important part of the implementation is that the resulting structure is isbitstype(...) = true, that allows to store it in parametric type as valtype.

One use case for this structure is to dispatch on and to replace begin or end (or more complex use cases, e.g. begin + 1) markers in constraints specification language.

source
diff --git a/dev/lib/message/index.html b/dev/lib/message/index.html index e310743ed..9a67f5e74 100644 --- a/dev/lib/message/index.html +++ b/dev/lib/message/index.html @@ -1,5 +1,5 @@ -Messages · ReactiveMP.jl
GitHub

Messages implementation

In our message passing framework one of the most important concepts is the message (wow!). Messages flow along edges of a factor graph and hold information about the part of the graph that it originates from. Usually this information is in a form of a probability distribution. Two common messages are belief propagation messages and variational messages, with are computed differently as shown below.

Abstract message type

Both belief propagation and variational messages are subtypes of a AbstractMessage supertype.

Belief-Propagation (or Sum-Product) message

message Belief propagation message

Variational message

message Variational message with structured factorisation q(x, y)q(z) assumption

Message type

All messages are encoded with the type Message.

ReactiveMP.Message — Type
Message{D, A} <: AbstractMessage

Message structure encodes a Belief Propagation message, which holds some data that usually a probability distribution, but can also be an arbitrary object. Message acts as a proxy structure to data object and proxies most of the statistical functions, e.g. mean, mode, cov etc.

Arguments

  • data::D: message always holds some data object associated with it
  • is_clamped::Bool, specifies if this message is clamped
  • is_initial::Bool, specifies if this message is initial
  • addons::A, specifies the addons of the message

Example

julia> distribution = Gamma(10.0, 2.0)
+Messages · ReactiveMP.jl
GitHub
Messages implementation
In our message passing framework one of the most important concepts is the message (wow!). Messages flow along edges of a factor graph and hold information about the part of the graph that it originates from. Usually this information is in a form of a probability distribution. Two common messages are belief propagation messages and variational messages, with are computed differently as shown below.
Abstract message type
Both belief propagation and variational messages are subtypes of a AbstractMessage supertype.
Belief-Propagation (or Sum-Product) message
message Belief propagation message
Variational message
message Variational message with structured factorisation q(x, y)q(z) assumption
Message type
All messages are encoded with the type Message. 
ReactiveMP.Message — Type
Message{D, A} <: AbstractMessage
Message structure encodes a Belief Propagation message, which holds some data that usually a probability distribution, but can also be an arbitrary object. Message acts as a proxy structure to data object and proxies most of the statistical functions, e.g. mean, mode, cov etc.
Arguments
  • data::D: message always holds some data object associated with it
  • is_clamped::Bool, specifies if this message is clamped
  • is_initial::Bool, specifies if this message is initial
  • addons::A, specifies the addons of the message
Example
julia> distribution = Gamma(10.0, 2.0)
 Distributions.Gamma{Float64}(α=10.0, θ=2.0)
 
 julia> message = Message(distribution, false, true, nothing)
@@ -16,5 +16,5 @@
 
 julia> is_initial(message)
 true
-
See also: AbstractMessage, ReactiveMP.materialize!
source
From an implementation point a view the Message structure does nothing but hold some data object and redirects most of the statistical related functions to that data object.  However, this object is used extensively in Julia's multiple dispatch.  Our implementation also uses extra is_initial and is_clamped fields to determine if product of two messages results in is_initial or is_clamped posterior marginal. The final field contains the addons. These contain additional information on top of the functional form of the distribution, such as its scaling or computation history.
distribution = NormalMeanPrecision(0.0, 1.0)
-message      = Message(distribution, false, true, nothing)
Message(ExponentialFamily.NormalMeanPrecision{Float64}(μ=0.0, w=1.0))
mean(message), precision(message)
(0.0, 1.0)
logpdf(message, 1.0)
-1.4189385332046727
is_clamped(message), is_initial(message)
(false, true)
The user should not really interact with Message structure while working with ReactiveMP unless doing some advanced inference procedures that involve prediction.

+

See also: AbstractMessage, ReactiveMP.materialize!

source

From an implementation point a view the Message structure does nothing but hold some data object and redirects most of the statistical related functions to that data object. However, this object is used extensively in Julia's multiple dispatch. Our implementation also uses extra is_initial and is_clamped fields to determine if product of two messages results in is_initial or is_clamped posterior marginal. The final field contains the addons. These contain additional information on top of the functional form of the distribution, such as its scaling or computation history.

distribution = NormalMeanPrecision(0.0, 1.0)
+message      = Message(distribution, false, true, nothing)
Message(ExponentialFamily.NormalMeanPrecision{Float64}(μ=0.0, w=1.0))
mean(message), precision(message)
(0.0, 1.0)
logpdf(message, 1.0)
-1.4189385332046727
is_clamped(message), is_initial(message)
(false, true)

The user should not really interact with Message structure while working with ReactiveMP unless doing some advanced inference procedures that involve prediction.

diff --git a/dev/lib/methods/index.html b/dev/lib/methods/index.html index e1321e6fa..1bd0ebf3a 100644 --- a/dev/lib/methods/index.html +++ b/dev/lib/methods/index.html @@ -1,5 +1,5 @@ -Exported methods · ReactiveMP.jl
GitHub

Using methods from ReactiveMP

In the Julia programming language (in contrast to Python for example) the most common way of loading a module is:

using ReactiveMP

A nice explanation about how modules/packages work in Julia can be found in the official documentation.

In a nutshell, Julia automatically resolves all name collisions and there is no a lot of benefit of importing specific names, e.g.:

import ReactiveMP: mean

One of the reasons for that is that Julia uses multiple-dispatch capabilities to merge names automatically and will indicate (with a warning) if something went wrong or names have unresolvable collisions on types. As a small example of this feature consider the following small import example:

import ReactiveMP: mean as mean_from_reactivemp
+Exported methods · ReactiveMP.jl
GitHub
Using methods from ReactiveMP
In the Julia programming language (in contrast to Python for example) the most common way of loading a module is:
using ReactiveMP
A nice explanation about how modules/packages work in Julia can be found in the official documentation.
In a nutshell, Julia automatically resolves all name collisions and there is no a lot of benefit of importing specific names, e.g.:
import ReactiveMP: mean
One of the reasons for that is that Julia uses multiple-dispatch capabilities to merge names automatically and will indicate (with a warning) if something went wrong or names have unresolvable collisions on types. As a small example of this feature consider the following small import example:
import ReactiveMP: mean as mean_from_reactivemp
 import Distributions: mean as mean_from_distributions
 import BayesBase: mean as mean_from_bayes_base
mean_from_reactivemp === mean_from_distributions
true
mean_from_reactivemp === mean_from_bayes_base
true
Even though we import mean function from different packages they actually refer to the same object. Worth noting that this is not always the case - Julia will print a warning in case it finds unresolvable conflicts and usage of such functions will be disallowed unless user import them specifically. Read more about this in the section of the Julia's documentation.
# It is easier to let Julia resolve names automatically
 # Julia will not overwrite `mean` that is coming from both packages
@@ -194,4 +194,4 @@
 softdot
 srcubature
 tiny
-update!

+update!
diff --git a/dev/lib/nodes/flow/index.html b/dev/lib/nodes/flow/index.html index 057e8d29d..9b05fa96d 100644 --- a/dev/lib/nodes/flow/index.html +++ b/dev/lib/nodes/flow/index.html @@ -1,9 +1,9 @@ -Flow · ReactiveMP.jl
GitHub

Flow node

See also Flow tutorial for a comprehensive guide on using flows in ReactiveMP.

ReactiveMP.PlanarFlow — Type

The PlanarFlow function is defined as

\[f({\bf{x}}) = {\bf{x}} + {\bf{u}} \tanh({\bf{w}}^\top {\bf{x}} + b)\]

with input and output dimension $D$. Here ${\bf{x}}\in \mathbb{R}^D$ represents the input of the function. Furthermore ${\bf{u}}\in \mathbb{R}^D$, ${\bf{w}}\in \mathbb{R}^D$ and $b\in\mathbb{R}$ represent the parameters of the function. The function contracts and expands the input space.

This function has been introduced in:

Rezende, Danilo, and Shakir Mohamed. "Variational inference with normalizing flows." International conference on machine learning. PMLR, 2015.

source
ReactiveMP.RadialFlow — Type

The RadialFlow function is defined as

\[f({\bf{x}}) = {\bf{x}} + \frac{\beta({bf{z}} - {\bf{z}}_0)}{\alpha + |{\bf{z}} - {\bf{z}}_0|}\]

with input and output dimension $D$. Here ${\bf{x}}\in \mathbb{R}^D$ represents the input of the function. Furthermore ${\bf{z}}_0\in \mathbb{R}^D$, $\alpha\in \mathbb{R}$ and $\beta\in\mathbb{R}$ represent the parameters of the function. The function contracts and expands the input space.

This function has been introduced in:

Rezende, Danilo, and Shakir Mohamed. "Variational inference with normalizing flows." International conference on machine learning. PMLR, 2015.

source
ReactiveMP.FlowModel — Type

The FlowModel structure is the most generic type of Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimensionality and a tuple of layers and can be constructed as FlowModel( dim, (layer1, layer2, ...) ).

Note: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.

source
ReactiveMP.CompiledFlowModel — Type

The CompiledFlowModel structure is the most generic type of compiled Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimension and a tuple of compiled layers. Do not manually create a CompiledFlowModel! Instead create a FlowModel first and compile it with compile(model::FlowModel). This will make sure that all layers/mappings are configured with the proper dimensionality and with randomly sampled parameters. Alternatively, if you would like to pass your own parameters, call compile(model::FlowModel, params::Vector).

Note: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.

source
ReactiveMP.compile — Function

compile() compiles a model by setting its parameters. It randomly sets parameter values in the layers and flows such that inference in the model can be obtained.

Input arguments

  • model::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.

Return arguments

  • ::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.
source

compile(model::FlowModel, params::Vector) lets you initialize a model model with a vector of parameters params.

Input arguments

  • model::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.
  • params::Vector - a vector of parameters with which the model should be compiled.

Return arguments

  • ::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.
source
ReactiveMP.AdditiveCouplingLayer — Type

The additive coupling layer specifies an invertible function ${\bf{y}} = g({\bf{x}})$ following the specific structure (for the mapping $g: \mathbb{R}^2 \rightarrow \mathbb{R}^2$):

\[ \begin{align} +Flow · ReactiveMP.jl

GitHub

Flow node

See also Flow tutorial for a comprehensive guide on using flows in ReactiveMP.

ReactiveMP.PlanarFlow — Type

The PlanarFlow function is defined as

\[f({\bf{x}}) = {\bf{x}} + {\bf{u}} \tanh({\bf{w}}^\top {\bf{x}} + b)\]

with input and output dimension $D$. Here ${\bf{x}}\in \mathbb{R}^D$ represents the input of the function. Furthermore ${\bf{u}}\in \mathbb{R}^D$, ${\bf{w}}\in \mathbb{R}^D$ and $b\in\mathbb{R}$ represent the parameters of the function. The function contracts and expands the input space.

This function has been introduced in:

Rezende, Danilo, and Shakir Mohamed. "Variational inference with normalizing flows." International conference on machine learning. PMLR, 2015.

source
ReactiveMP.RadialFlow — Type

The RadialFlow function is defined as

\[f({\bf{x}}) = {\bf{x}} + \frac{\beta({bf{z}} - {\bf{z}}_0)}{\alpha + |{\bf{z}} - {\bf{z}}_0|}\]

with input and output dimension $D$. Here ${\bf{x}}\in \mathbb{R}^D$ represents the input of the function. Furthermore ${\bf{z}}_0\in \mathbb{R}^D$, $\alpha\in \mathbb{R}$ and $\beta\in\mathbb{R}$ represent the parameters of the function. The function contracts and expands the input space.

This function has been introduced in:

Rezende, Danilo, and Shakir Mohamed. "Variational inference with normalizing flows." International conference on machine learning. PMLR, 2015.

source
ReactiveMP.FlowModel — Type

The FlowModel structure is the most generic type of Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimensionality and a tuple of layers and can be constructed as FlowModel( dim, (layer1, layer2, ...) ).

Note: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.

source
ReactiveMP.CompiledFlowModel — Type

The CompiledFlowModel structure is the most generic type of compiled Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimension and a tuple of compiled layers. Do not manually create a CompiledFlowModel! Instead create a FlowModel first and compile it with compile(model::FlowModel). This will make sure that all layers/mappings are configured with the proper dimensionality and with randomly sampled parameters. Alternatively, if you would like to pass your own parameters, call compile(model::FlowModel, params::Vector).

Note: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.

source
ReactiveMP.compile — Function

compile() compiles a model by setting its parameters. It randomly sets parameter values in the layers and flows such that inference in the model can be obtained.

Input arguments

  • model::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.

Return arguments

  • ::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.
source

compile(model::FlowModel, params::Vector) lets you initialize a model model with a vector of parameters params.

Input arguments

  • model::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.
  • params::Vector - a vector of parameters with which the model should be compiled.

Return arguments

  • ::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.
source
ReactiveMP.AdditiveCouplingLayer — Type

The additive coupling layer specifies an invertible function ${\bf{y}} = g({\bf{x}})$ following the specific structure (for the mapping $g: \mathbb{R}^2 \rightarrow \mathbb{R}^2$):

\[ \begin{align} y_1 &= x_1 \\ y_2 &= x_2 + f(x_1) \end{align}\]

where $f(\cdot)$ denotes an arbitrary function with mapping $f: \mathbb{R} \rightarrow \mathbb{R}$. This function can be chosen arbitrarily complex. Non-linear functions (neural networks) are often chosen to model complex relationships. From the definition of the model, invertibility can be easily achieved as

\[ \begin{align} x_1 &= y_1 \\ x_2 &= y_2 - f(y_1) \end{align}\]

The current implementation only allows for the mapping $g: \mathbb{R}^2 \rightarrow \mathbb{R}^2$, although this layer can be generalized for arbitrary input dimensions.

AdditiveCouplingLayer(f <: AbstractCouplingFlow) creates the layer structure with function f.

Example

f = PlanarFlow()
-layer = AdditiveCouplingLayer(f)

This layer structure has been introduced in:

Dinh, Laurent, David Krueger, and Yoshua Bengio. "Nice: Non-linear independent components estimation." arXiv preprint arXiv:1410.8516 (2014).

source
ReactiveMP.PermutationLayer — Type

The permutation layer specifies an invertible mapping ${\bf{y}} = g({\bf{x}}) = P{\bf{x}}$ where $P$ is a permutation matrix.

source
ReactiveMP.FlowMeta — Type

The FlowMeta structure contains the meta data of the Flow factor node. More specifically, it contains the model of the Flow factor node. The FlowMeta structure can be constructed as FlowMeta(model). Make sure that the flow model has been compiled.

The FlowMeta structure is required for the Flow factor node and can be included with the Flow node as: y ~ Flow(x) where { meta = FlowMeta(...) }

source
+layer = AdditiveCouplingLayer(f)

This layer structure has been introduced in:

Dinh, Laurent, David Krueger, and Yoshua Bengio. "Nice: Non-linear independent components estimation." arXiv preprint arXiv:1410.8516 (2014).

source
ReactiveMP.PermutationLayer — Type

The permutation layer specifies an invertible mapping ${\bf{y}} = g({\bf{x}}) = P{\bf{x}}$ where $P$ is a permutation matrix.

source
ReactiveMP.FlowMeta — Type

The FlowMeta structure contains the meta data of the Flow factor node. More specifically, it contains the model of the Flow factor node. The FlowMeta structure can be constructed as FlowMeta(model). Make sure that the flow model has been compiled.

The FlowMeta structure is required for the Flow factor node and can be included with the Flow node as: y ~ Flow(x) where { meta = FlowMeta(...) }

source
diff --git a/dev/lib/nodes/nodes/index.html b/dev/lib/nodes/nodes/index.html index f0b421cd3..4b50d0c7a 100644 --- a/dev/lib/nodes/nodes/index.html +++ b/dev/lib/nodes/nodes/index.html @@ -1,12 +1,12 @@ -Overview · ReactiveMP.jl
GitHub

Nodes implementation

In the message passing framework, one of the most important concepts is a factor node. A factor node represents a local function in a factorised representation of a generative model.

Note

To quickly check the list of all available factor nodes that can be used in the model specification language, call ?make_node or Base.doc(make_node).

Adding a custom node

ReactiveMP.jl exports the @node macro that allows for quick definition of a factor node with a fixed number of edges. The interface is the following:

struct MyNewCustomNode end
+Overview · ReactiveMP.jl
GitHub
Nodes implementation
In the message passing framework, one of the most important concepts is a factor node. A factor node represents a local function in a factorised representation of a generative model.
Note
To quickly check the list of all available factor nodes that can be used in the model specification language, call ?make_node or Base.doc(make_node).
Adding a custom node
ReactiveMP.jl exports the @node macro that allows for quick definition of a factor node with a fixed number of edges. The interface is the following:
struct MyNewCustomNode end
 
 @node MyNewCustomNode   Stochastic         [ x, y, z ]
 #     ^^^^^^^^^^^^^^^   ^^^^^^^^^^^^^      ^^^^^^^^^^^
 #     Node's tag/name   Node's type        A fixed set of edges
 #                       Another possible   The very first edge (in this example `x`) is considered
 #                       value is           to be the output of the node
-#                       `Deterministic`
This expression registers a new node that can be used with the inference engine. Note howeve, that the @node macro does not generate any message passing update rules. These must be defined using the @rule macro.
Node types
We distinguish different types of factor nodes in order to have better control over Bethe Free Energy computation. Each factor node has either the Deterministic or Stochastic functional form type.
For example the + node has the Deterministic type:
plus_node = make_node(+)
+#                       `Deterministic`
This expression registers a new node that can be used with the inference engine. Note howeve, that the @node macro does not generate any message passing update rules. These must be defined using the @rule macro.
Node types
We distinguish different types of factor nodes in order to have better control over Bethe Free Energy computation. Each factor node has either the Deterministic or Stochastic functional form type.
For example the + node has the Deterministic type:
plus_node = make_node(+)
 
 println("Is `+` node deterministic: ", isdeterministic(plus_node))
 println("Is `+` node stochastic: ", isstochastic(plus_node))
Is `+` node deterministic: true
@@ -16,7 +16,7 @@
 println("Is `Bernoulli` node stochastic: ", isstochastic(bernoulli_node))
Is `Bernoulli` node deterministic: false
 Is `Bernoulli` node stochastic: true
To get an actual instance of the type object we use sdtype function:
println("sdtype() of `+` node is ", sdtype(plus_node))
 println("sdtype() of `Bernoulli` node is ", sdtype(bernoulli_node))
sdtype() of `+` node is Deterministic()
-sdtype() of `Bernoulli` node is Stochastic()
Node functional dependencies pipeline
The generic implementation of factor nodes in ReactiveMP supports custom functional dependency pipelines. Briefly, the functional dependencies pipeline defines what dependencies are need to compute a single message. As an example, consider the belief-propagation message update equation for a factor node $f$ with three edges: $x$, $y$ and $z$:
\[\mu(x) = \int \mu(y) \mu(z) f(x, y, z) \mathrm{d}y \mathrm{d}z\]
Here we see that in the standard setting for the belief-propagation message out of edge $x$, we need only messages from the edges $y$ and $z$. In contrast, consider the variational message update rule equation with mean-field assumption:
\[\mu(x) = \exp \int q(y) q(z) \log f(x, y, z) \mathrm{d}y \mathrm{d}z\]
We see that in this setting, we do not need messages $\mu(y)$ and $\mu(z)$, but only the marginals $q(y)$ and $q(z)$. The purpose of a functional dependencies pipeline is to determine functional dependencies (a set of messages or marginals) that are needed to compute a single message. By default, ReactiveMP.jl uses so-called DefaultFunctionalDependencies that correctly implements belief-propagation and variational message passing schemes (including both mean-field and structured factorisations). The full list of built-in pipelines is presented below:
ReactiveMP.RequireMessageFunctionalDependencies — Type
RequireMessageFunctionalDependencies(indices::Tuple, start_with::Tuple)
The same as DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the inbound message on the this edge.
Arguments
  • indices::Tuple, tuple of integers, which indicates what edges should require inbound messages
  • start_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial inbound messages for edges in indices
Note: start_with uses setmessage! mechanism, hence, it can be visible by other listeners on the same edge. Explicit call to setmessage! overwrites whatever has been passed in start_with.
@model macro accepts a simplified construction of this pipeline:
@model function some_model()
+sdtype() of `Bernoulli` node is Stochastic()
Node functional dependencies pipeline
The generic implementation of factor nodes in ReactiveMP supports custom functional dependency pipelines. Briefly, the functional dependencies pipeline defines what dependencies are need to compute a single message. As an example, consider the belief-propagation message update equation for a factor node $f$ with three edges: $x$, $y$ and $z$:
\[\mu(x) = \int \mu(y) \mu(z) f(x, y, z) \mathrm{d}y \mathrm{d}z\]
Here we see that in the standard setting for the belief-propagation message out of edge $x$, we need only messages from the edges $y$ and $z$. In contrast, consider the variational message update rule equation with mean-field assumption:
\[\mu(x) = \exp \int q(y) q(z) \log f(x, y, z) \mathrm{d}y \mathrm{d}z\]
We see that in this setting, we do not need messages $\mu(y)$ and $\mu(z)$, but only the marginals $q(y)$ and $q(z)$. The purpose of a functional dependencies pipeline is to determine functional dependencies (a set of messages or marginals) that are needed to compute a single message. By default, ReactiveMP.jl uses so-called DefaultFunctionalDependencies that correctly implements belief-propagation and variational message passing schemes (including both mean-field and structured factorisations). The full list of built-in pipelines is presented below:
ReactiveMP.RequireMessageFunctionalDependencies — Type
RequireMessageFunctionalDependencies(indices::Tuple, start_with::Tuple)
The same as DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the inbound message on the this edge.
Arguments
  • indices::Tuple, tuple of integers, which indicates what edges should require inbound messages
  • start_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial inbound messages for edges in indices
Note: start_with uses setmessage! mechanism, hence, it can be visible by other listeners on the same edge. Explicit call to setmessage! overwrites whatever has been passed in start_with.
@model macro accepts a simplified construction of this pipeline:
@model function some_model()
     # ...
     y ~ NormalMeanVariance(x, τ) where {
         pipeline = RequireMessage(x = vague(NormalMeanPrecision),     τ)
@@ -25,7 +25,7 @@
                                   # and initialise with `vague(...)`  but here we skip initialisation
     }
     # ...
-end
Deprecation warning: RequireInboundFunctionalDependencies has been deprecated in favor of RequireMessageFunctionalDependencies.
See also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMarginalFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies
source
ReactiveMP.RequireMarginalFunctionalDependencies — Type
RequireMarginalFunctionalDependencies(indices::Tuple, start_with::Tuple)
Similar to DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the posterior marginal on that edge.
Arguments
  • indices::Tuple, tuple of integers, which indicates what edges should require their own marginals
  • start_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial marginal for edges in indices
Note: start_with uses the setmarginal! mechanism, hence it can be visible to other listeners on the same edge. Explicit calls to setmarginal! overwrites whatever has been passed in start_with.
@model macro accepts a simplified construction of this pipeline:
@model function some_model()
+end
Deprecation warning: RequireInboundFunctionalDependencies has been deprecated in favor of RequireMessageFunctionalDependencies.
See also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMarginalFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies
source
ReactiveMP.RequireMarginalFunctionalDependencies — Type
RequireMarginalFunctionalDependencies(indices::Tuple, start_with::Tuple)
Similar to DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the posterior marginal on that edge.
Arguments
  • indices::Tuple, tuple of integers, which indicates what edges should require their own marginals
  • start_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial marginal for edges in indices
Note: start_with uses the setmarginal! mechanism, hence it can be visible to other listeners on the same edge. Explicit calls to setmarginal! overwrites whatever has been passed in start_with.
@model macro accepts a simplified construction of this pipeline:
@model function some_model()
     # ...
     y ~ NormalMeanVariance(x, τ) where {
         pipeline = RequireMarginal(x = vague(NormalMeanPrecision),     τ)
@@ -34,4 +34,4 @@
                                    # and initialise with `vague(...)`  but here we skip initialisation
     }
     # ...
-end
Note: The simplified construction in @model macro syntax is only available in GraphPPL.jl of version >2.2.0.
See also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMessageFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies
source
ReactiveMP.RequireEverythingFunctionalDependencies — Type
RequireEverythingFunctionalDependencies
This pipeline specifies that in order to compute a message of some edge update rules request everything that is available locally. This includes all inbound messages (including on the same edge) and marginals over all local edge-clusters (this may or may not include marginals on single edges, depends on the local factorisation constraint).
See also: DefaultFunctionalDependencies, RequireMessageFunctionalDependencies, RequireMarginalFunctionalDependencies
source
Node traits
Each factor node has to define the ReactiveMP.as_node_functional_form trait function and to specify a ReactiveMP.ValidNodeFunctionalForm  singleton as a return object. By default ReactiveMP.as_node_functional_form returns ReactiveMP.UndefinedNodeFunctionalForm.  Objects that do not specify this property correctly cannot be used in model specification.
Note
@node macro does that automatically

+end

Note: The simplified construction in @model macro syntax is only available in GraphPPL.jl of version >2.2.0.

See also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMessageFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies

source
ReactiveMP.RequireEverythingFunctionalDependencies — Type

RequireEverythingFunctionalDependencies

This pipeline specifies that in order to compute a message of some edge update rules request everything that is available locally. This includes all inbound messages (including on the same edge) and marginals over all local edge-clusters (this may or may not include marginals on single edges, depends on the local factorisation constraint).

See also: DefaultFunctionalDependencies, RequireMessageFunctionalDependencies, RequireMarginalFunctionalDependencies

source

Node traits

Each factor node has to define the ReactiveMP.as_node_functional_form trait function and to specify a ReactiveMP.ValidNodeFunctionalForm singleton as a return object. By default ReactiveMP.as_node_functional_form returns ReactiveMP.UndefinedNodeFunctionalForm. Objects that do not specify this property correctly cannot be used in model specification.

Note

@node macro does that automatically

ReactiveMP.ValidNodeFunctionalForm — Type
ValidNodeFunctionalForm

Trait specification for an object that can be used in model specification as a factor node.

See also: ReactiveMP.as_node_functional_form, ReactiveMP.UndefinedNodeFunctionalForm

source
ReactiveMP.UndefinedNodeFunctionalForm — Type
UndefinedNodeFunctionalForm

Trait specification for an object that can not be used in model specification as a factor node.

See also: ReactiveMP.as_node_functional_form, ReactiveMP.ValidNodeFunctionalForm

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ReactiveMP.as_node_functional_form — Function
as_node_functional_form(object)

Determines object node functional form trait specification. Returns either ValidNodeFunctionalForm() or UndefinedNodeFunctionalForm().

See also: ReactiveMP.ValidNodeFunctionalForm, ReactiveMP.UndefinedNodeFunctionalForm

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diff --git a/dev/lib/rules/rules/index.html b/dev/lib/rules/rules/index.html index d580c2393..fdb10df20 100644 --- a/dev/lib/rules/rules/index.html +++ b/dev/lib/rules/rules/index.html @@ -1,13 +1,13 @@ -Message update rules · ReactiveMP.jl
GitHub

Rules implementation

Message update rules

ReactiveMP.rule — Function
rule(fform, on, vconstraint, mnames, messages, qnames, marginals, meta, __node)

This function is used to compute an outbound message for a given node

Arguments

  • fform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)
  • on: Outbound interface's tag for which a message has to be computed, e.g. ::Val{:out} or ::Val{:μ}
  • vconstraint: Variable constraints for an outbound interface, e.g. Marginalisation or MomentMatching
  • mnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • messages: Tuple of message of the same length as mnames used to compute an outbound message
  • qnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • marginals: Tuple of marginals of the same length as qnames used to compute an outbound message
  • meta: Extra meta information
  • addons: Extra addons information
  • __node: Node reference

For all available rules, see ReactiveMP.print_rules_table().

See also: @rule, marginalrule, @marginalrule

source
ReactiveMP.@rule — Macro
@rule NodeType(:Edge, Constraint) (Arguments..., [ meta::MetaType ]) = begin
+Message update rules · ReactiveMP.jl
GitHub
Rules implementation
Message update rules
ReactiveMP.rule — Function
rule(fform, on, vconstraint, mnames, messages, qnames, marginals, meta, __node)
This function is used to compute an outbound message for a given node
Arguments
  • fform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)
  • on: Outbound interface's tag for which a message has to be computed, e.g. ::Val{:out} or ::Val{:μ}
  • vconstraint: Variable constraints for an outbound interface, e.g. Marginalisation or MomentMatching
  • mnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • messages: Tuple of message of the same length as mnames used to compute an outbound message
  • qnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • marginals: Tuple of marginals of the same length as qnames used to compute an outbound message
  • meta: Extra meta information
  • addons: Extra addons information
  • __node: Node reference
For all available rules, see ReactiveMP.print_rules_table().
See also: @rule, marginalrule, @marginalrule
source
ReactiveMP.@rule — Macro
@rule NodeType(:Edge, Constraint) (Arguments..., [ meta::MetaType ]) = begin
     # rule body
     return ...
 end
The @rule macro help to define new methods for the rule function. It works particularly well in combination with the @node macro. It has a specific structure, which must specify:
  • NodeType: must be a valid Julia type. If some attempt to define a rule for a Julia function (for example +), use typeof(+)
  • Edge: edge label, usually edge labels are defined with the @node macro
  • Constrain: DEPRECATED, please just use the Marginalisation label
  • Arguments: defines a list of the input arguments for the rule
    • m_* prefix indicates that the argument is of type Message from the edge *
    • q_* prefix indicates that the argument is of type Marginal on the edge *
  • Meta::MetaType - optionally, a user can specify a Meta object of type MetaType.  This can be useful is some attempts to try different rules with different approximation methods or if the rule itself requires some temporary storage or cache.  The default meta is nothing.
Here are various examples of the @rule macro usage:
  1. Belief-Propagation (or Sum-Product) message update rule for the NormalMeanVariance node  toward the :μ edge with the Marginalisation constraint. Input arguments are m_out and m_v, which are the messages from the corresponding edges out and v and have the type PointMass.
@rule NormalMeanVariance(:μ, Marginalisation) (m_out::PointMass, m_v::PointMass) = NormalMeanVariance(mean(m_out), mean(m_v))
  1. Mean-field message update rule for the NormalMeanVariance node towards the :μ edge with the Marginalisation constraint. Input arguments are q_out and q_v, which are the marginals on the corresponding edges out and v of type Any.
@rule NormalMeanVariance(:μ, Marginalisation) (q_out::Any, q_v::Any) = NormalMeanVariance(mean(q_out), mean(q_v))
  1. Structured Variational message update rule for the NormalMeanVariance node towards the :out edge with the Marginalisation constraint. Input arguments are m_μ, which is a message from the μ edge of type UnivariateNormalDistributionsFamily, and q_v, which is a marginal on the v edge of type Any.
@rule NormalMeanVariance(:out, Marginalisation) (m_μ::UnivariateNormalDistributionsFamily, q_v::Any) = begin
     m_μ_mean, m_μ_cov = mean_cov(m_μ)
     return NormalMeanVariance(m_μ_mean, m_μ_cov + mean(q_v))
-end
See also: rule, marginalrule, [@marginalrule], @call_rule
source
ReactiveMP.@call_rule — Macro
@call_rule NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])
The @call_rule macro helps to call the rule method with an easier syntax.  The structure of the macro is almost the same as in the @rule macro, but there is no begin ... end block, but instead each argument must have a specified value with the = operator.
The @call_rule accepts optional list of options before the functional form specification, for example:
@call_rule [ return_addons = true ] NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])
The list of available options is:
  • return_addons - forces the @call_rule to return the tuple of (result, addons)
See also: @rule, rule, @call_marginalrule
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ReactiveMP.@call_rule — Macro
@call_rule NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])
The @call_rule macro helps to call the rule method with an easier syntax.  The structure of the macro is almost the same as in the @rule macro, but there is no begin ... end block, but instead each argument must have a specified value with the = operator.
The @call_rule accepts optional list of options before the functional form specification, for example:
@call_rule [ return_addons = true ] NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])
The list of available options is:
  • return_addons - forces the @call_rule to return the tuple of (result, addons)
See also: @rule, rule, @call_marginalrule
source
ReactiveMP.print_rules_table — Function
Prints a table of all message passing update rules in ReactiveMP.
 
-Use `Markdown.parse` on the output of this function to get a prettified table.
source
Marginal update rules
ReactiveMP.marginalrule — Function
marginalrule(fform, on, mnames, messages, qnames, marginals, meta, __node)
This function is used to compute a local joint marginal for a given node
Arguments
  • fform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)
  • on: Local joint marginal tag , e.g. ::Val{ :mean_precision } or ::Val{ :out_mean_precision }
  • mnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • messages: Tuple of message of the same length as mnames used to compute an outbound message
  • qnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • marginals: Tuple of marginals of the same length as qnames used to compute an outbound message
  • meta: Extra meta information
  • __node: Node reference
See also: rule, @rule @marginalrule
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ReactiveMP.@marginalrule — Macro
@marginalrule NodeType(:Cluster) (Arguments..., [ meta::MetaType ]) = begin
+Use `Markdown.parse` on the output of this function to get a prettified table.
source
Marginal update rules
ReactiveMP.marginalrule — Function
marginalrule(fform, on, mnames, messages, qnames, marginals, meta, __node)
This function is used to compute a local joint marginal for a given node
Arguments
  • fform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)
  • on: Local joint marginal tag , e.g. ::Val{ :mean_precision } or ::Val{ :out_mean_precision }
  • mnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • messages: Tuple of message of the same length as mnames used to compute an outbound message
  • qnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • marginals: Tuple of marginals of the same length as qnames used to compute an outbound message
  • meta: Extra meta information
  • __node: Node reference
See also: rule, @rule @marginalrule
source
ReactiveMP.@marginalrule — Macro
@marginalrule NodeType(:Cluster) (Arguments..., [ meta::MetaType ]) = begin
     # rule body
     return ...
 end
The @marginalrule macro help to define new methods for the marginalrule function. It works particularly well in combination with the @node macro. It has a specific structure, which must specify:
  • NodeType: must be a valid Julia type. If some attempt to define a rule for a Julia function (for example +), use typeof(+)
  • Cluster: edge cluster that contains joined edge labels with the _ symbol. Usually edge labels are defined with the @node macro
  • Arguments: defines a list of the input arguments for the rule
    • m_* prefix indicates that the argument is of type Message from the edge *
    • q_* prefix indicates that the argument is of type Marginal on the edge *
  • Meta::MetaType - optionally, a user can specify a Meta object of type MetaType.  This can be useful is some attempts to try different rules with different approximation methods or if the rule itself requires some temporary storage or cache.  The default meta is nothing.
The @marginalrule can return a NamedTuple in the return statement. This would indicate some variables in the joint marginal  for the Cluster are independent and the joint itself is factorised. For example if some attempts to compute a marginal for the q(x, y) it is possible to return (x = ..., y = ...) as the result of the computation to indicate that q(x, y) = q(x)q(y).
Here are various examples of the @marginalrule macro usage:
  1. Marginal computation rule around the NormalMeanPrecision node for the q(out, μ). The rule accepts arguments m_out and m_μ, which are the messages 
from the out and μ edges respectively, and q_τ which is the marginal on the edge τ.
@marginalrule NormalMeanPrecision(:out_μ) (m_out::UnivariateNormalDistributionsFamily, m_μ::UnivariateNormalDistributionsFamily, q_τ::Any) = begin
@@ -22,7 +22,7 @@
     return MvNormalWeightedMeanPrecision(xi, W)
 end
  1. Marginal computation rule around the NormalMeanPrecision node for the q(out, μ). The rule accepts arguments m_out and m_μ, which are the messages from the 
out and μ edges respectively, and q_τ which is the marginal on the edge τ. In this example the result of the computation is a NamedTuple
@marginalrule NormalMeanPrecision(:out_μ) (m_out::PointMass, m_μ::UnivariateNormalDistributionsFamily, q_τ::Any) = begin
     return (out = m_out, μ = prod(ClosedProd(), NormalMeanPrecision(mean(m_out), mean(q_τ)), m_μ))
-end
source
ReactiveMP.@call_marginalrule — Macro
@call_marginalrule NodeType(:edge) (argument1 = value1, argument2 = value2, ..., [ meta = ... ])
The @call_marginalrule macro helps to call the marginalrule method with an easier syntax.  The structure of the macro is almost the same as in the @marginalrule macro, but there is no begin ... end block,  but instead each argument must have a specified value with the = operator.
See also: @marginalrule, marginalrule, @call_rule
source
Testing utilities for the update rules
ReactiveMP.@test_rules — Macro
@test_rules [options] rule [ test_entries... ]
The @test_rules macro generates test cases for message update rules for probabilistic programming models that follow the "message passing" paradigm. It takes a rule specification as input and generates a set of tests based on that specification. This macro is provided by ReactiveMP.
Note: The Test module must be imported explicitly. The @test_rules macro tries to use the @test macro, which must be defined globally.
Arguments
The macro takes three arguments:
  • options: An optional argument that specifies the options for the test generation process. See below for details.
  • rule: A rule specification in the same format as the @rule macro, e.g. Beta(:out, Marginalisation) or NormalMeanVariance(:μ, Marginalisation).
  • test_entries: An array of named tuples (input = ..., output = ...). The input entry has the same format as the input for the @rule macro. The output entry specifies the expected output.
Options
The following options are available:
  • check_type_promotion: By default, this option is set to false. If set to true, the macro generates an extensive list of extra tests that aim to check the correct type promotion within the tests. For example, if all inputs are of type Float32, then the expected output should also be of type Float32. See the paramfloattype and convert_paramfloattype functions for details.
  • atol: Sets the desired accuracy for the tests. The tests use the custom_rule_isapprox function from ReactiveMP to check if outputs are approximately the same. This argument can be either a single number or an array of key => value pairs.
  • extra_float_types: A set of extra float types to be used in the check_type_promotion tests. This argument has no effect if check_type_promotion is set to false.
The default values for the atol option are:
  • Float32: 1e-4
  • Float64: 1e-6
  • BigFloat: 1e-8
Examples

+end
source
ReactiveMP.@call_marginalrule — Macro
@call_marginalrule NodeType(:edge) (argument1 = value1, argument2 = value2, ..., [ meta = ... ])
The @call_marginalrule macro helps to call the marginalrule method with an easier syntax.  The structure of the macro is almost the same as in the @marginalrule macro, but there is no begin ... end block,  but instead each argument must have a specified value with the = operator.
See also: @marginalrule, marginalrule, @call_rule
source
Testing utilities for the update rules
ReactiveMP.@test_rules — Macro
@test_rules [options] rule [ test_entries... ]
The @test_rules macro generates test cases for message update rules for probabilistic programming models that follow the "message passing" paradigm. It takes a rule specification as input and generates a set of tests based on that specification. This macro is provided by ReactiveMP.
Note: The Test module must be imported explicitly. The @test_rules macro tries to use the @test macro, which must be defined globally.
Arguments
The macro takes three arguments:
  • options: An optional argument that specifies the options for the test generation process. See below for details.
  • rule: A rule specification in the same format as the @rule macro, e.g. Beta(:out, Marginalisation) or NormalMeanVariance(:μ, Marginalisation).
  • test_entries: An array of named tuples (input = ..., output = ...). The input entry has the same format as the input for the @rule macro. The output entry specifies the expected output.
Options
The following options are available:
  • check_type_promotion: By default, this option is set to false. If set to true, the macro generates an extensive list of extra tests that aim to check the correct type promotion within the tests. For example, if all inputs are of type Float32, then the expected output should also be of type Float32. See the paramfloattype and convert_paramfloattype functions for details.
  • atol: Sets the desired accuracy for the tests. The tests use the custom_rule_isapprox function from ReactiveMP to check if outputs are approximately the same. This argument can be either a single number or an array of key => value pairs.
  • extra_float_types: A set of extra float types to be used in the check_type_promotion tests. This argument has no effect if check_type_promotion is set to false.
The default values for the atol option are:
  • Float32: 1e-4
  • Float64: 1e-6
  • BigFloat: 1e-8
Examples

 @test_rules [check_type_promotion = true] Beta(:out, Marginalisation) [
     (input = (m_a = PointMass(1.0), m_b = PointMass(2.0)), output = Beta(1.0, 2.0)),
     (input = (m_a = PointMass(2.0), m_b = PointMass(2.0)), output = Beta(2.0, 2.0)),
@@ -33,6 +33,6 @@
     (input = (q_a = PointMass(1.0), q_b = PointMass(2.0)), output = Beta(1.0, 2.0)),
     (input = (q_a = PointMass(2.0), q_b = PointMass(2.0)), output = Beta(2.0, 2.0)),
     (input = (q_a = PointMass(3.0), q_b = PointMass(3.0)), output = Beta(3.0, 3.0))
-]
See also: ReactiveMP.@test_marginalrules
source
Table of available update rules
using ReactiveMP, Markdown
-Markdown.parse(ReactiveMP.print_rules_table())
NodeOutputInputsMeta
*inμ(A) :: BayesBase.PointMass{<:LinearAlgebra.UniformScaling}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
*inμ(A) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: BayesBase.PointMass{<:Real}
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecision
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: ExponentialFamily.MvNormalMeanPrecision
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: ExponentialFamily.MvNormalMeanCovariance
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*inμ(out) :: Distributions.UnivariateDistributionUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: Distributions.UnivariateDistribution
*inμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*inμ(out) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass
*inμ(out) :: ExponentialFamily.GammaDistributionsFamilyUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:Real}
*inμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractMatrix}
*inμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractVector}
*inμ(out) :: FUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:Real}
*inμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractMatrix}
*inμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractVector}
*outμ(A) :: Distributions.UnivariateDistributionUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Distributions.UnivariateDistribution
*outμ(A) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*outμ(A) :: BayesBase.PointMass{<:LinearAlgebra.UniformScaling}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
*outμ(A) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.MvNormalWeightedMeanPrecision
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.MvNormalMeanPrecision
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.MvNormalMeanCovariance
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*outμ(A) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.GammaDistributionsFamily
*outμ(A) :: ExponentialFamily.GammaDistributionsFamilyUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*outμ(A) :: BayesBase.PointMass{<:AbstractMatrix}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: F
*outμ(A) :: FUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractMatrix}
*outμ(A) :: BayesBase.PointMass{<:AbstractVector}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*outμ(A) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractVector}
*Aμ(out) :: Distributions.UnivariateDistributionUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Distributions.UnivariateDistribution
*Aμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*Aμ(out) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass
*Aμ(out) :: ExponentialFamily.GammaDistributionsFamilyUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*Aμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractMatrix}
*Aμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractVector}
*Aμ(out) :: FUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*Aμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractMatrix}
*Aμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractVector}
+in2μ(out) :: Any
μ(in1) :: Any
-in2μ(out) :: Any
μ(in1) :: Any
-in1μ(out) :: Any
μ(in2) :: Any
-outμ(in1) :: Any
μ(in2) :: Any
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision
+in1μ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T1, M, P} where {M<:AbstractVector{T1}, P<:AbstractMatrix{T1}}Nothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T1, M, P} where {M<:AbstractVector{T1}, P<:AbstractMatrix{T1}}Nothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T2, M, P} where {M<:AbstractVector{T2}, P<:AbstractMatrix{T2}}
+in1μ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+in1μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+in1μ(out) :: ExponentialFamily.MvNormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalMeanPrecision
+in1μ(out) :: ExponentialFamily.NormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.NormalMeanPrecision
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+in1μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision
+outμ(in1) :: ExponentialFamily.MvNormalWeightedMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T1, M, P} where {M<:AbstractVector{T1}, P<:AbstractMatrix{T1}}Nothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T2, M, P} where {M<:AbstractVector{T2}, P<:AbstractMatrix{T2}}
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalMeanPrecision
+outμ(in1) :: ExponentialFamily.MvNormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.NormalMeanPrecision
+outμ(in1) :: ExponentialFamily.NormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Distributions.DistributionNothing
μ(in2) :: Distributions.Distribution
dotin2μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in1) :: BayesBase.PointMass
dotin1μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in2) :: BayesBase.PointMass
dotoutμ(in1) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in2) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
dotoutμ(in1) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in2) :: BayesBase.PointMass
GCVyq(x) :: AnyUnion{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
GCVyμ(x) :: Union{ReactiveMP.ExponentialLinearQuadratic, ExponentialFamily.UnivariateNormalDistributionsFamily, ExponentialFamily.UnivariateGaussianDistributionsFamily}Union{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
GCVzq(y) :: AnyGCVMetadata
q(x) :: Any
q(κ) :: Any
q(ω) :: Any
GCVzq(y_x) :: AnyGCVMetadata
q(κ) :: Any
q(ω) :: Any
GCVκq(y) :: AnyGCVMetadata
q(x) :: Any
q(z) :: Any
q(ω) :: Any
GCVκq(y_x) :: AnyGCVMetadata
q(z) :: Any
q(ω) :: Any
GCVωq(y) :: AnyGCVMetadata
q(x) :: Any
q(z) :: Any
q(κ) :: Any
GCVωq(y_x) :: AnyGCVMetadata
q(z) :: Any
q(κ) :: Any
GCVxq(y) :: AnyUnion{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
GCVxμ(y) :: Union{ReactiveMP.ExponentialLinearQuadratic, ExponentialFamily.UnivariateNormalDistributionsFamily, ExponentialFamily.UnivariateGaussianDistributionsFamily}Union{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
Distributions.Bernoullipq(out) :: Distributions.Categorical{P} where P<:RealNothing
Distributions.Bernoullipq(out) :: Distributions.BernoulliNothing
Distributions.Bernoullipq(out) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realpq(out) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realpq(out) :: AnyNothing
Distributions.Bernoullipμ(out) :: BayesBase.PointMassNothing
Distributions.Bernoullioutq(p) :: AnyNothing
Distributions.Bernoullioutq(p) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutq(p) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutq(p) :: Distributions.DirichletNothing
Distributions.Bernoullioutμ(p) :: BayesBase.PointMassNothing
Distributions.Bernoullioutμ(p) :: Distributions.BetaNothing
Distributions.Categorical{P} where P<:Realoutμ(p) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutμ(p) :: Distributions.DirichletNothing
ARyq(x) :: AnyARMeta
q(θ) :: Any
q(γ) :: Any
ARyμ(x) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TARMeta
q(θ) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
q(γ) :: Any
ARθq(y) :: AnyARMeta
q(x) :: Any
q(γ) :: Any
ARθq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TARMeta
q(γ) :: Any
ARxq(y) :: AnyARMeta
q(θ) :: Any
q(γ) :: Any
ARxμ(y) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TARMeta
q(θ) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
q(γ) :: Any
ARγq(y) :: AnyARMeta
q(x) :: Any
q(θ) :: Any
ARγq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TARMeta
q(θ) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
InverseWishartoutq(ν) :: AnyNothing
q(S) :: Any
Distributions.Wishartoutq(ν) :: AnyNothing
q(S) :: Any
InverseWishartoutμ(ν) :: BayesBase.PointMassNothing
q(S) :: Any
Distributions.Wishartoutμ(ν) :: BayesBase.PointMassNothing
q(S) :: Any
InverseWishartoutμ(S) :: BayesBase.PointMassNothing
q(ν) :: Any
Distributions.Wishartoutμ(S) :: BayesBase.PointMassNothing
q(ν) :: Any
InverseWishartoutμ(ν) :: BayesBase.PointMassNothing
μ(S) :: BayesBase.PointMass
Distributions.Wishartoutμ(ν) :: BayesBase.PointMassNothing
μ(S) :: BayesBase.PointMass
ExponentialFamily.MatrixDirichletoutq(a) :: BayesBase.PointMassNothing
Distributions.Dirichletoutq(a) :: BayesBase.PointMass{<:AbstractVector}Nothing
ExponentialFamily.MatrixDirichletoutμ(a) :: BayesBase.PointMassNothing
Distributions.Dirichletoutμ(a) :: BayesBase.PointMass{<:AbstractVector}Nothing
Mixtureswitchμ(out) :: AnyNothing
μ(inputs) :: ManyOf{<:Tuple{Vararg{Any, N}}}
Mixtureoutμ(inputs) :: ManyOf{<:Tuple{Vararg{Any, N}}}Nothing
q(switch) :: BayesBase.PointMass
Mixtureoutμ(switch) :: AnyNothing
μ(inputs) :: ManyOf{<:Tuple{Vararg{Any, N}}}
Mixtureinputsμ(out) :: AnyNothing
q(switch) :: BayesBase.PointMass
Mixtureinputsμ(out) :: AnyNothing
μ(switch) :: Any
GammaMixture{N}outq(switch) :: AnyNothing
q(a) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(b) :: ExponentialFamily.GammaDistributionsFamily
GammaMixturebq(out) :: AnyNothing
q(switch) :: Any
q(a) :: Any
GammaMixtureaq(out) :: AnyNothing
q(switch) :: Any
q(b) :: ExponentialFamily.GammaDistributionsFamily
GammaMixture{N}switchq(out) :: AnyNothing
q(a) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(b) :: ExponentialFamily.GammaDistributionsFamily
NormalMixture{N}outq(switch) :: AnyNothing
q(m) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(p) :: ManyOf{<:Tuple{Vararg{Any, N}}}
NormalMixturepq(out) :: AnyNothing
q(switch) :: Any
q(m) :: Any
NormalMixturemq(out) :: AnyNothing
q(switch) :: Any
q(p) :: Any
NormalMixture{N}switchq(out) :: AnyNothing
q(m) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(p) :: ManyOf{<:Tuple{Vararg{Any, N}}}
BIFMHelperoutq(in) :: AnyNothing
Probitoutq(in) :: BayesBase.PointMassUnion{Nothing, ProbitMeta}
BIFMHelperinμ(out) :: AnyNothing
NOTinμ(out) :: Distributions.BernoulliNothing
Probitinμ(out) :: Union{BayesBase.PointMass, Distributions.Bernoulli}Union{Nothing, ProbitMeta}
Flowinμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TFlowMeta{M, <:Unscented}
Flowinμ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecisionFlowMeta{M, <:Linearization}
Flowinμ(out) :: ExponentialFamily.MvNormalMeanPrecisionFlowMeta{M, <:Linearization}
Flowinμ(out) :: ExponentialFamily.MvNormalMeanCovarianceFlowMeta{M, <:Linearization}
Flowoutμ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TFlowMeta{M, <:Unscented}
Flowoutμ(in) :: ExponentialFamily.MvNormalWeightedMeanPrecisionFlowMeta{M, <:Linearization}
Flowoutμ(in) :: ExponentialFamily.MvNormalMeanPrecisionFlowMeta{M, <:Linearization}
Flowoutμ(in) :: ExponentialFamily.MvNormalMeanCovarianceFlowMeta{M, <:Linearization}
Distributions.Gammaoutq(α) :: AnyNothing
q(θ) :: Any
Distributions.InverseGammaoutq(α) :: AnyNothing
q(θ) :: Any
Distributions.Gammaoutμ(α) :: BayesBase.PointMassNothing
μ(θ) :: BayesBase.PointMass
Distributions.InverseGammaoutμ(α) :: BayesBase.PointMassNothing
μ(θ) :: BayesBase.PointMass
ExponentialFamily.GammaShapeRateαq(out) :: Distributions.GammaNothing
q(β) :: Distributions.Gamma
ExponentialFamily.GammaShapeRateoutq(α) :: BayesBase.PointMassNothing
q(β) :: BayesBase.PointMass
ExponentialFamily.GammaShapeRateoutμ(α) :: BayesBase.PointMassNothing
μ(β) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceΣq(out_μ) :: AnyNothing
ExponentialFamily.MvNormalMeanCovarianceΣq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: BayesBase.PointMassNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutq(μ) :: BayesBase.PointMassNothing
q(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceoutq(μ) :: AnyNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: BayesBase.PointMassNothing
μ(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: BayesBase.PointMassNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceμq(out) :: BayesBase.PointMassNothing
q(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceμq(out) :: AnyNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: BayesBase.PointMassNothing
μ(Σ) :: BayesBase.PointMass
MvNormalMeanScalePrecisionγq(out_μ) :: AnyNothing
MvNormalMeanScalePrecisionγq(out) :: AnyNothing
q(μ) :: Any
MvNormalMeanScalePrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(γ) :: Any
MvNormalMeanScalePrecisionμq(out) :: AnyNothing
q(γ) :: Any
MvNormalMeanScalePrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(γ) :: Any
MvNormalMeanScalePrecisionoutq(μ) :: AnyNothing
q(γ) :: Any
ExponentialFamily.MvNormalMeanPrecisionΛq(out_μ) :: AnyNothing
ExponentialFamily.MvNormalMeanPrecisionΛq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Distributions.Wishart
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutq(μ) :: BayesBase.PointMassNothing
q(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionoutq(μ) :: AnyNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Distributions.Wishart
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionμq(out) :: BayesBase.PointMassNothing
q(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionμq(out) :: AnyNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
μ(Λ) :: BayesBase.PointMass
Poissonoutq(l) :: ExponentialFamily.GammaDistributionsFamilyNothing
Poissonoutμ(l) :: BayesBase.PointMassNothing
Poissonlq(out) :: AnyNothing
Poissonlμ(out) :: BayesBase.PointMassNothing
HalfNormaloutq(v) :: BayesBase.PointMassNothing
ExponentialFamily.MvNormalWeightedMeanPrecisionoutq(ξ) :: AnyNothing
q(Λ) :: Any
ExponentialFamily.MvNormalWeightedMeanPrecisionoutμ(ξ) :: BayesBase.PointMassNothing
μ(Λ) :: BayesBase.PointMass
ORoutμ(in1) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
ANDoutμ(in1) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
ORin2μ(out) :: Distributions.BernoulliNothing
μ(in1) :: Distributions.Bernoulli
ANDin2μ(out) :: Distributions.BernoulliNothing
μ(in1) :: Distributions.Bernoulli
ORin1μ(out) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
ANDin1μ(out) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
Distributions.Uniformoutμ(a) :: BayesBase.PointMassNothing
μ(b) :: BayesBase.PointMass
Distributions.Betaoutμ(a) :: BayesBase.PointMassNothing
μ(b) :: BayesBase.PointMass
Distributions.Uniformoutμ(b) :: BayesBase.PointMassNothing
q(a) :: BayesBase.PointMass
Distributions.Uniformoutμ(a) :: BayesBase.PointMassNothing
q(b) :: BayesBase.PointMass
Distributions.Uniformoutq(a) :: BayesBase.PointMassNothing
q(b) :: BayesBase.PointMass
Distributions.Betaoutq(a) :: BayesBase.PointMassNothing
q(b) :: BayesBase.PointMass
NOToutμ(in) :: Distributions.BernoulliNothing
Probitoutμ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, ProbitMeta}
Probitoutμ(in) :: BayesBase.PointMassUnion{Nothing, ProbitMeta}
ContinuousTransitionWq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
ContinuousTransitionaq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
q(W) :: Any
ContinuousTransitionxμ(y) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
q(W) :: Any
ContinuousTransitionyμ(x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
q(W) :: Any
SoftDotγq(y) :: AnyNothing
q(θ) :: Any
q(x) :: Any
SoftDotθq(y) :: AnyNothing
q(x) :: Any
q(γ) :: Any
SoftDotxq(y) :: AnyNothing
q(θ) :: Any
q(γ) :: Any
SoftDotyq(θ) :: AnyNothing
q(x) :: Any
q(γ) :: Any
BIFMzprevμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
μ(znext) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
BIFMznextμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
μ(zprev) :: BayesBase.TerminalProdArgument{<:Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T}
BIFMoutμ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(zprev) :: BayesBase.TerminalProdArgument{<:Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T}
μ(znext) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
BIFMinμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(zprev) :: BayesBase.TerminalProdArgument{<:Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T}
μ(znext) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
ExponentialFamily.NormalMeanVariancevq(out_μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
ExponentialFamily.NormalMeanVariancevq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.NormalMeanVariancevμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
ExponentialFamily.NormalMeanVariancevμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(μ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVariancevμ(out) :: BayesBase.PointMassNothing
μ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
ExponentialFamily.NormalMeanVarianceμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceμμ(out) :: BayesBase.PointMassNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
q(v) :: Any
ExponentialFamily.NormalMeanVarianceμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμμ(out) :: BayesBase.PointMassNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμq(out) :: BayesBase.PointMassNothing
q(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμq(out) :: AnyNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: BayesBase.PointMassNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutq(μ) :: BayesBase.PointMassNothing
q(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceoutq(μ) :: AnyNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: BayesBase.PointMassNothing
μ(v) :: BayesBase.PointMass
Transitioninq(out) :: BayesBase.PointMassNothing
q(a) :: BayesBase.PointMass
Transitioninq(out) :: AnyNothing
q(a) :: ExponentialFamily.MatrixDirichlet
Transitioninμ(out) :: Distributions.Categorical{P} where P<:Real
q(a) :: BayesBase.PointMass
Transitioninμ(out) :: Distributions.Categorical{P} where P<:RealNothing
q(a) :: ExponentialFamily.MatrixDirichlet
Transitioninμ(out) :: Distributions.Categorical{P} where P<:RealNothing
μ(a) :: BayesBase.PointMass
Transitionoutμ(in) :: Distributions.DiscreteNonParametric
q(a) :: BayesBase.PointMass
Transitionoutμ(in) :: Distributions.Categorical{P} where P<:RealNothing
q(a) :: Distributions.Distribution{Distributions.Matrixvariate, Distributions.Continuous}
Transitionoutq(in) :: Distributions.Categorical{P} where P<:RealNothing
q(a) :: Any
Transitionoutq(in) :: BayesBase.PointMassNothing
q(a) :: BayesBase.PointMass
Transitionoutμ(in) :: Distributions.Categorical{P} where P<:RealNothing
μ(a) :: BayesBase.PointMass
Transitionaq(out_in) :: BayesBase.ContingencyNothing
Transitionaq(out) :: AnyNothing
q(in) :: Distributions.Categorical{P} where P<:Real
Probitinμ(out) :: Union{BayesBase.PointMass, Distributions.Bernoulli}Union{Nothing, ProbitMeta}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
Probitinμ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, ProbitMeta}
q(out) :: BayesBase.PointMass
Probitinq(out) :: BayesBase.PointMassUnion{Nothing, ProbitMeta}
ExponentialFamily.NormalMeanPrecisionτq(out_μ) :: AnyNothing
ExponentialFamily.NormalMeanPrecisionτq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμq(out) :: BayesBase.PointMassNothing
q(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμq(out) :: AnyNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutq(μ) :: BayesBase.PointMassNothing
q(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionoutq(μ) :: AnyNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
μ(τ) :: BayesBase.PointMass
DeltaFnoutq(ins) :: BayesBase.FactorizedJoint{P}DeltaMeta{M}
DeltaFnoutq(ins) :: BayesBase.FactorizedJointDeltaMeta{M}
DeltaFnoutμ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M}
DeltaFnoutμ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M}
DeltaFninμ(in) :: AnyDeltaMeta{M}
q(ins) :: BayesBase.FactorizedJoint
DeltaFninμ(in) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, Nothing}
q(ins) :: ExponentialFamily.JointNormal
DeltaFninμ(in) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
q(ins) :: ExponentialFamily.JointNormal
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Nothing
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Nothing
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
outμ(in1) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
in2μ(out) :: Distributions.BernoulliNothing
μ(in1) :: Distributions.Bernoulli
in1μ(out) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli

-

+]

See also: ReactiveMP.@test_marginalrules

source

Table of available update rules

using ReactiveMP, Markdown
+Markdown.parse(ReactiveMP.print_rules_table())
NodeOutputInputsMeta
*inμ(A) :: BayesBase.PointMass{<:LinearAlgebra.UniformScaling}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
*inμ(A) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: BayesBase.PointMass{<:Real}
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecision
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: ExponentialFamily.MvNormalMeanPrecision
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: ExponentialFamily.MvNormalMeanCovariance
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*inμ(out) :: Distributions.UnivariateDistributionUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: Distributions.UnivariateDistribution
*inμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*inμ(out) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass
*inμ(out) :: ExponentialFamily.GammaDistributionsFamilyUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:Real}
*inμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractMatrix}
*inμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractVector}
*inμ(out) :: FUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:Real}
*inμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractMatrix}
*inμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractVector}
*outμ(A) :: Distributions.UnivariateDistributionUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Distributions.UnivariateDistribution
*outμ(A) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*outμ(A) :: BayesBase.PointMass{<:LinearAlgebra.UniformScaling}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
*outμ(A) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.MvNormalWeightedMeanPrecision
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.MvNormalMeanPrecision
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.MvNormalMeanCovariance
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*outμ(A) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.GammaDistributionsFamily
*outμ(A) :: ExponentialFamily.GammaDistributionsFamilyUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*outμ(A) :: BayesBase.PointMass{<:AbstractMatrix}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: F
*outμ(A) :: FUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractMatrix}
*outμ(A) :: BayesBase.PointMass{<:AbstractVector}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*outμ(A) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractVector}
*Aμ(out) :: Distributions.UnivariateDistributionUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Distributions.UnivariateDistribution
*Aμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*Aμ(out) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass
*Aμ(out) :: ExponentialFamily.GammaDistributionsFamilyUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*Aμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractMatrix}
*Aμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractVector}
*Aμ(out) :: FUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*Aμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractMatrix}
*Aμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractVector}
+in2μ(out) :: Any
μ(in1) :: Any
-in2μ(out) :: Any
μ(in1) :: Any
-in1μ(out) :: Any
μ(in2) :: Any
-outμ(in1) :: Any
μ(in2) :: Any
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision
+in1μ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T1, M, P} where {M<:AbstractVector{T1}, P<:AbstractMatrix{T1}}Nothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T1, M, P} where {M<:AbstractVector{T1}, P<:AbstractMatrix{T1}}Nothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T2, M, P} where {M<:AbstractVector{T2}, P<:AbstractMatrix{T2}}
+in1μ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+in1μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+in1μ(out) :: ExponentialFamily.MvNormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalMeanPrecision
+in1μ(out) :: ExponentialFamily.NormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.NormalMeanPrecision
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+in1μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision
+outμ(in1) :: ExponentialFamily.MvNormalWeightedMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T1, M, P} where {M<:AbstractVector{T1}, P<:AbstractMatrix{T1}}Nothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T2, M, P} where {M<:AbstractVector{T2}, P<:AbstractMatrix{T2}}
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalMeanPrecision
+outμ(in1) :: ExponentialFamily.MvNormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.NormalMeanPrecision
+outμ(in1) :: ExponentialFamily.NormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Distributions.DistributionNothing
μ(in2) :: Distributions.Distribution
dotin2μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in1) :: BayesBase.PointMass
dotin1μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in2) :: BayesBase.PointMass
dotoutμ(in1) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in2) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
dotoutμ(in1) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in2) :: BayesBase.PointMass
NOToutμ(in) :: Distributions.BernoulliNothing
Probitoutμ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, ProbitMeta}
Probitoutμ(in) :: BayesBase.PointMassUnion{Nothing, ProbitMeta}
NOTinμ(out) :: Distributions.BernoulliNothing
BIFMHelperinμ(out) :: AnyNothing
Probitinμ(out) :: Union{BayesBase.PointMass, Distributions.Bernoulli}Union{Nothing, ProbitMeta}
ExponentialFamily.NormalMeanVariancevq(out_μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
ExponentialFamily.NormalMeanVariancevq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.NormalMeanVariancevμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
ExponentialFamily.NormalMeanVariancevμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(μ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVariancevμ(out) :: BayesBase.PointMassNothing
μ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
ExponentialFamily.NormalMeanVarianceμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceμμ(out) :: BayesBase.PointMassNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
q(v) :: Any
ExponentialFamily.NormalMeanVarianceμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμμ(out) :: BayesBase.PointMassNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμq(out) :: BayesBase.PointMassNothing
q(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμq(out) :: AnyNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: BayesBase.PointMassNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutq(μ) :: BayesBase.PointMassNothing
q(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceoutq(μ) :: AnyNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: BayesBase.PointMassNothing
μ(v) :: BayesBase.PointMass
GammaMixture{N}outq(switch) :: AnyNothing
q(a) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(b) :: ExponentialFamily.GammaDistributionsFamily
GammaMixturebq(out) :: AnyNothing
q(switch) :: Any
q(a) :: Any
GammaMixtureaq(out) :: AnyNothing
q(switch) :: Any
q(b) :: ExponentialFamily.GammaDistributionsFamily
GammaMixture{N}switchq(out) :: AnyNothing
q(a) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(b) :: ExponentialFamily.GammaDistributionsFamily
Distributions.Betaoutq(a) :: BayesBase.PointMassNothing
q(b) :: BayesBase.PointMass
Distributions.Uniformoutq(a) :: BayesBase.PointMassNothing
q(b) :: BayesBase.PointMass
Distributions.Betaoutμ(a) :: BayesBase.PointMassNothing
μ(b) :: BayesBase.PointMass
Distributions.Uniformoutμ(a) :: BayesBase.PointMassNothing
μ(b) :: BayesBase.PointMass
BIFMHelperoutq(in) :: AnyNothing
Probitoutq(in) :: BayesBase.PointMassUnion{Nothing, ProbitMeta}
Distributions.Bernoullipq(out) :: Distributions.Categorical{P} where P<:RealNothing
Distributions.Bernoullipq(out) :: Distributions.BernoulliNothing
Distributions.Bernoullipq(out) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realpq(out) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realpq(out) :: AnyNothing
Distributions.Bernoullipμ(out) :: BayesBase.PointMassNothing
Distributions.Bernoullioutq(p) :: AnyNothing
Distributions.Bernoullioutq(p) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutq(p) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutq(p) :: Distributions.DirichletNothing
Distributions.Bernoullioutμ(p) :: BayesBase.PointMassNothing
Distributions.Bernoullioutμ(p) :: Distributions.BetaNothing
Distributions.Categorical{P} where P<:Realoutμ(p) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutμ(p) :: Distributions.DirichletNothing
Transitioninq(out) :: BayesBase.PointMassNothing
q(a) :: BayesBase.PointMass
Transitioninq(out) :: AnyNothing
q(a) :: ExponentialFamily.MatrixDirichlet
Transitioninμ(out) :: Distributions.Categorical{P} where P<:Real
q(a) :: BayesBase.PointMass
Transitioninμ(out) :: Distributions.Categorical{P} where P<:RealNothing
q(a) :: ExponentialFamily.MatrixDirichlet
Transitioninμ(out) :: Distributions.Categorical{P} where P<:RealNothing
μ(a) :: BayesBase.PointMass
Transitionoutμ(in) :: Distributions.DiscreteNonParametric
q(a) :: BayesBase.PointMass
Transitionoutμ(in) :: Distributions.Categorical{P} where P<:RealNothing
q(a) :: Distributions.Distribution{Distributions.Matrixvariate, Distributions.Continuous}
Transitionoutq(in) :: Distributions.Categorical{P} where P<:RealNothing
q(a) :: Any
Transitionoutq(in) :: BayesBase.PointMassNothing
q(a) :: BayesBase.PointMass
Transitionoutμ(in) :: Distributions.Categorical{P} where P<:RealNothing
μ(a) :: BayesBase.PointMass
Transitionaq(out_in) :: BayesBase.ContingencyNothing
Transitionaq(out) :: AnyNothing
q(in) :: Distributions.Categorical{P} where P<:Real
ContinuousTransitionWq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
ContinuousTransitionaq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
q(W) :: Any
ContinuousTransitionxμ(y) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
q(W) :: Any
ContinuousTransitionyμ(x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
q(W) :: Any
ANDoutμ(in1) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
ORoutμ(in1) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
ANDin2μ(out) :: Distributions.BernoulliNothing
μ(in1) :: Distributions.Bernoulli
ORin2μ(out) :: Distributions.BernoulliNothing
μ(in1) :: Distributions.Bernoulli
ANDin1μ(out) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
ORin1μ(out) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
DeltaFnoutq(ins) :: BayesBase.FactorizedJoint{P}DeltaMeta{M}
DeltaFnoutq(ins) :: BayesBase.FactorizedJointDeltaMeta{M}
DeltaFnoutμ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M}
DeltaFnoutμ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M}
DeltaFninμ(in) :: AnyDeltaMeta{M}
q(ins) :: BayesBase.FactorizedJoint
DeltaFninμ(in) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, Nothing}
q(ins) :: ExponentialFamily.JointNormal
DeltaFninμ(in) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
q(ins) :: ExponentialFamily.JointNormal
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Nothing
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Nothing
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
InverseWishartoutq(ν) :: AnyNothing
q(S) :: Any
Distributions.Wishartoutq(ν) :: AnyNothing
q(S) :: Any
InverseWishartoutμ(ν) :: BayesBase.PointMassNothing
q(S) :: Any
Distributions.Wishartoutμ(ν) :: BayesBase.PointMassNothing
q(S) :: Any
InverseWishartoutμ(S) :: BayesBase.PointMassNothing
q(ν) :: Any
Distributions.Wishartoutμ(S) :: BayesBase.PointMassNothing
q(ν) :: Any
InverseWishartoutμ(ν) :: BayesBase.PointMassNothing
μ(S) :: BayesBase.PointMass
Distributions.Wishartoutμ(ν) :: BayesBase.PointMassNothing
μ(S) :: BayesBase.PointMass
ExponentialFamily.MatrixDirichletoutq(a) :: BayesBase.PointMassNothing
Distributions.Dirichletoutq(a) :: BayesBase.PointMass{<:AbstractVector}Nothing
ExponentialFamily.MatrixDirichletoutμ(a) :: BayesBase.PointMassNothing
Distributions.Dirichletoutμ(a) :: BayesBase.PointMass{<:AbstractVector}Nothing
MvNormalMeanScalePrecisionγq(out_μ) :: AnyNothing
MvNormalMeanScalePrecisionγq(out) :: AnyNothing
q(μ) :: Any
MvNormalMeanScalePrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(γ) :: Any
MvNormalMeanScalePrecisionμq(out) :: AnyNothing
q(γ) :: Any
MvNormalMeanScalePrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(γ) :: Any
MvNormalMeanScalePrecisionoutq(μ) :: AnyNothing
q(γ) :: Any
BIFMzprevμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
μ(znext) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
BIFMznextμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
μ(zprev) :: BayesBase.TerminalProdArgument{<:Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T}
BIFMoutμ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(zprev) :: BayesBase.TerminalProdArgument{<:Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T}
μ(znext) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
BIFMinμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(zprev) :: BayesBase.TerminalProdArgument{<:Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T}
μ(znext) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
Flowinμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TFlowMeta{M, <:Unscented}
Flowinμ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecisionFlowMeta{M, <:Linearization}
Flowinμ(out) :: ExponentialFamily.MvNormalMeanPrecisionFlowMeta{M, <:Linearization}
Flowinμ(out) :: ExponentialFamily.MvNormalMeanCovarianceFlowMeta{M, <:Linearization}
Flowoutμ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TFlowMeta{M, <:Unscented}
Flowoutμ(in) :: ExponentialFamily.MvNormalWeightedMeanPrecisionFlowMeta{M, <:Linearization}
Flowoutμ(in) :: ExponentialFamily.MvNormalMeanPrecisionFlowMeta{M, <:Linearization}
Flowoutμ(in) :: ExponentialFamily.MvNormalMeanCovarianceFlowMeta{M, <:Linearization}
ExponentialFamily.MvNormalMeanCovarianceΣq(out_μ) :: AnyNothing
ExponentialFamily.MvNormalMeanCovarianceΣq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: BayesBase.PointMassNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutq(μ) :: BayesBase.PointMassNothing
q(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceoutq(μ) :: AnyNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: BayesBase.PointMassNothing
μ(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: BayesBase.PointMassNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceμq(out) :: BayesBase.PointMassNothing
q(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceμq(out) :: AnyNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: BayesBase.PointMassNothing
μ(Σ) :: BayesBase.PointMass
Distributions.InverseGammaoutq(α) :: AnyNothing
q(θ) :: Any
Distributions.Gammaoutq(α) :: AnyNothing
q(θ) :: Any
Distributions.InverseGammaoutμ(α) :: BayesBase.PointMassNothing
μ(θ) :: BayesBase.PointMass
Distributions.Gammaoutμ(α) :: BayesBase.PointMassNothing
μ(θ) :: BayesBase.PointMass
GCVyq(x) :: AnyUnion{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
GCVyμ(x) :: Union{ReactiveMP.ExponentialLinearQuadratic, ExponentialFamily.UnivariateNormalDistributionsFamily, ExponentialFamily.UnivariateGaussianDistributionsFamily}Union{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
GCVzq(y) :: AnyGCVMetadata
q(x) :: Any
q(κ) :: Any
q(ω) :: Any
GCVzq(y_x) :: AnyGCVMetadata
q(κ) :: Any
q(ω) :: Any
GCVκq(y) :: AnyGCVMetadata
q(x) :: Any
q(z) :: Any
q(ω) :: Any
GCVκq(y_x) :: AnyGCVMetadata
q(z) :: Any
q(ω) :: Any
GCVωq(y) :: AnyGCVMetadata
q(x) :: Any
q(z) :: Any
q(κ) :: Any
GCVωq(y_x) :: AnyGCVMetadata
q(z) :: Any
q(κ) :: Any
GCVxq(y) :: AnyUnion{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
GCVxμ(y) :: Union{ReactiveMP.ExponentialLinearQuadratic, ExponentialFamily.UnivariateNormalDistributionsFamily, ExponentialFamily.UnivariateGaussianDistributionsFamily}Union{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
ExponentialFamily.MvNormalWeightedMeanPrecisionoutq(ξ) :: AnyNothing
q(Λ) :: Any
ExponentialFamily.MvNormalWeightedMeanPrecisionoutμ(ξ) :: BayesBase.PointMassNothing
μ(Λ) :: BayesBase.PointMass
ARyq(x) :: AnyARMeta
q(θ) :: Any
q(γ) :: Any
ARyμ(x) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TARMeta
q(θ) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
q(γ) :: Any
ARθq(y) :: AnyARMeta
q(x) :: Any
q(γ) :: Any
ARθq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TARMeta
q(γ) :: Any
ARxq(y) :: AnyARMeta
q(θ) :: Any
q(γ) :: Any
ARxμ(y) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TARMeta
q(θ) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
q(γ) :: Any
ARγq(y) :: AnyARMeta
q(x) :: Any
q(θ) :: Any
ARγq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TARMeta
q(θ) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
Mixtureswitchμ(out) :: AnyNothing
μ(inputs) :: ManyOf{<:Tuple{Vararg{Any, N}}}
Mixtureoutμ(inputs) :: ManyOf{<:Tuple{Vararg{Any, N}}}Nothing
q(switch) :: BayesBase.PointMass
Mixtureoutμ(switch) :: AnyNothing
μ(inputs) :: ManyOf{<:Tuple{Vararg{Any, N}}}
Mixtureinputsμ(out) :: AnyNothing
q(switch) :: BayesBase.PointMass
Mixtureinputsμ(out) :: AnyNothing
μ(switch) :: Any
HalfNormaloutq(v) :: BayesBase.PointMassNothing
NormalMixture{N}outq(switch) :: AnyNothing
q(m) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(p) :: ManyOf{<:Tuple{Vararg{Any, N}}}
NormalMixturepq(out) :: AnyNothing
q(switch) :: Any
q(m) :: Any
NormalMixturemq(out) :: AnyNothing
q(switch) :: Any
q(p) :: Any
NormalMixture{N}switchq(out) :: AnyNothing
q(m) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(p) :: ManyOf{<:Tuple{Vararg{Any, N}}}
SoftDotγq(y) :: AnyNothing
q(θ) :: Any
q(x) :: Any
SoftDotθq(y) :: AnyNothing
q(x) :: Any
q(γ) :: Any
SoftDotxq(y) :: AnyNothing
q(θ) :: Any
q(γ) :: Any
SoftDotyq(θ) :: AnyNothing
q(x) :: Any
q(γ) :: Any
ExponentialFamily.GammaShapeRateαq(out) :: Distributions.GammaNothing
q(β) :: Distributions.Gamma
ExponentialFamily.GammaShapeRateoutq(α) :: BayesBase.PointMassNothing
q(β) :: BayesBase.PointMass
ExponentialFamily.GammaShapeRateoutμ(α) :: BayesBase.PointMassNothing
μ(β) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionΛq(out_μ) :: AnyNothing
ExponentialFamily.MvNormalMeanPrecisionΛq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Distributions.Wishart
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutq(μ) :: BayesBase.PointMassNothing
q(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionoutq(μ) :: AnyNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Distributions.Wishart
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionμq(out) :: BayesBase.PointMassNothing
q(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionμq(out) :: AnyNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionτq(out_μ) :: AnyNothing
ExponentialFamily.NormalMeanPrecisionτq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμq(out) :: BayesBase.PointMassNothing
q(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμq(out) :: AnyNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutq(μ) :: BayesBase.PointMassNothing
q(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionoutq(μ) :: AnyNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
μ(τ) :: BayesBase.PointMass
Probitinμ(out) :: Union{BayesBase.PointMass, Distributions.Bernoulli}Union{Nothing, ProbitMeta}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
Probitinμ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, ProbitMeta}
q(out) :: BayesBase.PointMass
Probitinq(out) :: BayesBase.PointMassUnion{Nothing, ProbitMeta}
Distributions.Uniformoutμ(b) :: BayesBase.PointMassNothing
q(a) :: BayesBase.PointMass
Distributions.Uniformoutμ(a) :: BayesBase.PointMassNothing
q(b) :: BayesBase.PointMass
Poissonoutq(l) :: ExponentialFamily.GammaDistributionsFamilyNothing
Poissonoutμ(l) :: BayesBase.PointMassNothing
Poissonlq(out) :: AnyNothing
Poissonlμ(out) :: BayesBase.PointMassNothing
outμ(in1) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
in2μ(out) :: Distributions.BernoulliNothing
μ(in1) :: Distributions.Bernoulli
in1μ(out) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
+
diff --git a/stable b/stable index 516ff7a78..6afce1303 120000 --- a/stable +++ b/stable @@ -1 +1 @@ -v3.13.0 \ No newline at end of file +v3.13.3 \ No newline at end of file diff --git a/v3 b/v3 index 516ff7a78..6afce1303 120000 --- a/v3 +++ b/v3 @@ -1 +1 @@ -v3.13.0 \ No newline at end of file +v3.13.3 \ No newline at end of file diff --git a/v3.13 b/v3.13 index 516ff7a78..6afce1303 120000 --- a/v3.13 +++ b/v3.13 @@ -1 +1 @@ -v3.13.0 \ No newline at end of file +v3.13.3 \ No newline at end of file diff --git a/v3.13.1/.documenter-siteinfo.json b/v3.13.1/.documenter-siteinfo.json new file mode 100644 index 000000000..4bc5b782a --- /dev/null +++ b/v3.13.1/.documenter-siteinfo.json @@ -0,0 +1 @@ +{"documenter":{"julia_version":"1.10.0","generation_timestamp":"2024-01-12T15:00:45","documenter_version":"1.2.1"}} \ No newline at end of file diff --git a/v3.13.1/assets/documenter.js b/v3.13.1/assets/documenter.js new file mode 100644 index 000000000..f5311607b --- /dev/null +++ b/v3.13.1/assets/documenter.js @@ -0,0 +1,889 @@ +// Generated by Documenter.jl +requirejs.config({ + paths: { + 'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia.min', + 'headroom': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/headroom.min', + 'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.13.2/jquery-ui.min', + 'minisearch': 'https://cdn.jsdelivr.net/npm/minisearch@6.1.0/dist/umd/index.min', + 'katex-auto-render': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/contrib/auto-render.min', + 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.0/jquery.min', + 'headroom-jquery': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/jQuery.headroom.min', + 'katex': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/katex.min', + 'highlight': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min', + 'highlight-julia-repl': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia-repl.min', + }, + shim: { + "highlight-julia": { + "deps": [ + "highlight" + ] + }, + "katex-auto-render": { + "deps": [ + "katex" + ] + }, + "headroom-jquery": { + "deps": [ + "jquery", + "headroom" + ] + }, + "highlight-julia-repl": { + "deps": [ + "highlight" + ] + } +} +}); +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'katex', 'katex-auto-render'], function($, katex, renderMathInElement) { +$(document).ready(function() { + renderMathInElement( + document.body, + { + "delimiters": [ + { + "left": "$", + "right": "$", + "display": false + }, + { + "left": "$$", + "right": "$$", + "display": true + }, + { + "left": "\\[", + "right": "\\]", + "display": true + } + ] +} + + ); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'highlight', 'highlight-julia', 'highlight-julia-repl'], function($) { +$(document).ready(function() { + hljs.highlightAll(); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +let timer = 0; +var isExpanded = true; + +$(document).on("click", ".docstring header", function () { + let articleToggleTitle = "Expand docstring"; + + debounce(() => { + if ($(this).siblings("section").is(":visible")) { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + } else { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + articleToggleTitle = "Collapse docstring"; + } + + $(this) + .find(".docstring-article-toggle-button") + .prop("title", articleToggleTitle); + $(this).siblings("section").slideToggle(); + }); +}); + +$(document).on("click", ".docs-article-toggle-button", function () { + let articleToggleTitle = "Expand docstring"; + let navArticleToggleTitle = "Expand all docstrings"; + + debounce(() => { + if (isExpanded) { + $(this).removeClass("fa-chevron-up").addClass("fa-chevron-down"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + + isExpanded = false; + + $(".docstring section").slideUp(); + } else { + $(this).removeClass("fa-chevron-down").addClass("fa-chevron-up"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + isExpanded = true; + articleToggleTitle = "Collapse docstring"; + navArticleToggleTitle = "Collapse all docstrings"; + + $(".docstring section").slideDown(); + } + + $(this).prop("title", navArticleToggleTitle); + $(".docstring-article-toggle-button").prop("title", articleToggleTitle); + }); +}); + +function debounce(callback, timeout = 300) { + if (Date.now() - timer > timeout) { + callback(); + } + + clearTimeout(timer); + + timer = Date.now(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require([], function() { +function addCopyButtonCallbacks() { + for (const el of document.getElementsByTagName("pre")) { + const button = document.createElement("button"); + button.classList.add("copy-button", "fa-solid", "fa-copy"); + button.setAttribute("aria-label", "Copy this code block"); + button.setAttribute("title", "Copy"); + + el.appendChild(button); + + const success = function () { + button.classList.add("success", "fa-check"); + button.classList.remove("fa-copy"); + }; + + const failure = function () { + button.classList.add("error", "fa-xmark"); + button.classList.remove("fa-copy"); + }; + + button.addEventListener("click", function () { + copyToClipboard(el.innerText).then(success, failure); + + setTimeout(function () { + button.classList.add("fa-copy"); + button.classList.remove("success", "fa-check", "fa-xmark"); + }, 5000); + }); + } +} + +function copyToClipboard(text) { + // clipboard API is only available in secure contexts + if (window.navigator && window.navigator.clipboard) { + return window.navigator.clipboard.writeText(text); + } else { + return new Promise(function (resolve, reject) { + try { + const el = document.createElement("textarea"); + el.textContent = text; + el.style.position = "fixed"; + el.style.opacity = 0; + document.body.appendChild(el); + el.select(); + document.execCommand("copy"); + + resolve(); + } catch (err) { + reject(err); + } finally { + document.body.removeChild(el); + } + }); + } +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", addCopyButtonCallbacks); +} else { + addCopyButtonCallbacks(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'headroom', 'headroom-jquery'], function($, Headroom) { + +// Manages the top navigation bar (hides it when the user starts scrolling down on the +// mobile). +window.Headroom = Headroom; // work around buggy module loading? +$(document).ready(function () { + $("#documenter .docs-navbar").headroom({ + tolerance: { up: 10, down: 10 }, + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'minisearch'], function($, minisearch) { + +// In general, most search related things will have "search" as a prefix. +// To get an in-depth about the thought process you can refer: https://hetarth02.hashnode.dev/series/gsoc + +let results = []; +let timer = undefined; + +let data = documenterSearchIndex["docs"].map((x, key) => { + x["id"] = key; // minisearch requires a unique for each object + return x; +}); + +// list below is the lunr 2.1.3 list minus the intersect with names(Base) +// (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with) +// ideally we'd just filter the original list but it's not available as a variable +const stopWords = new Set([ + "a", + "able", + "about", + "across", + "after", + "almost", + "also", + "am", + "among", + "an", + "and", + "are", + "as", + "at", + "be", + "because", + "been", + "but", + "by", + "can", + "cannot", + "could", + "dear", + "did", + "does", + "either", + "ever", + "every", + "from", + "got", + "had", + "has", + "have", + "he", + "her", + "hers", + "him", + "his", + "how", + "however", + "i", + "if", + "into", + "it", + "its", + "just", + "least", + "like", + "likely", + "may", + "me", + "might", + "most", + "must", + "my", + "neither", + "no", + "nor", + "not", + "of", + "off", + "often", + "on", + "or", + "other", + "our", + "own", + "rather", + "said", + "say", + "says", + "she", + "should", + "since", + "so", + "some", + "than", + "that", + "the", + "their", + "them", + "then", + "there", + "these", + "they", + "this", + "tis", + "to", + "too", + "twas", + "us", + "wants", + "was", + "we", + "were", + "what", + "when", + "who", + "whom", + "why", + "will", + "would", + "yet", + "you", + "your", +]); + +let index = new minisearch({ + fields: ["title", "text"], // fields to index for full-text search + storeFields: ["location", "title", "text", "category", "page"], // fields to return with search results + processTerm: (term) => { + let word = stopWords.has(term) ? null : term; + if (word) { + // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names + word = word + .replace(/^[^a-zA-Z0-9@!]+/, "") + .replace(/[^a-zA-Z0-9@!]+$/, ""); + } + + return word ?? null; + }, + // add . as a separator, because otherwise "title": "Documenter.Anchors.add!", would not find anything if searching for "add!", only for the entire qualification + tokenize: (string) => string.split(/[\s\-\.]+/), + // options which will be applied during the search + searchOptions: { + boost: { title: 100 }, + fuzzy: 2, + processTerm: (term) => { + let word = stopWords.has(term) ? null : term; + if (word) { + word = word + .replace(/^[^a-zA-Z0-9@!]+/, "") + .replace(/[^a-zA-Z0-9@!]+$/, ""); + } + + return word ?? null; + }, + tokenize: (string) => string.split(/[\s\-\.]+/), + }, +}); + +index.addAll(data); + +let filters = [...new Set(data.map((x) => x.category))]; +var modal_filters = make_modal_body_filters(filters); +var filter_results = []; + +$(document).on("keyup", ".documenter-search-input", function (event) { + // Adding a debounce to prevent disruptions from super-speed typing! + debounce(() => update_search(filter_results), 300); +}); + +$(document).on("click", ".search-filter", function () { + if ($(this).hasClass("search-filter-selected")) { + $(this).removeClass("search-filter-selected"); + } else { + $(this).addClass("search-filter-selected"); + } + + // Adding a debounce to prevent disruptions from crazy clicking! + debounce(() => get_filters(), 300); +}); + +/** + * A debounce function, takes a function and an optional timeout in milliseconds + * + * @function callback + * @param {number} timeout + */ +function debounce(callback, timeout = 300) { + clearTimeout(timer); + timer = setTimeout(callback, timeout); +} + +/** + * Make/Update the search component + * + * @param {string[]} selected_filters + */ +function update_search(selected_filters = []) { + let initial_search_body = ` +
Type something to get started!
+ `; + + let querystring = $(".documenter-search-input").val(); + + if (querystring.trim()) { + results = index.search(querystring, { + filter: (result) => { + // Filtering results + if (selected_filters.length === 0) { + return result.score >= 1; + } else { + return ( + result.score >= 1 && selected_filters.includes(result.category) + ); + } + }, + }); + + let search_result_container = ``; + let search_divider = `
`; + + if (results.length) { + let links = []; + let count = 0; + let search_results = ""; + + results.forEach(function (result) { + if (result.location) { + // Checking for duplication of results for the same page + if (!links.includes(result.location)) { + search_results += make_search_result(result, querystring); + count++; + } + + links.push(result.location); + } + }); + + let result_count = `
${count} result(s)
`; + + search_result_container = ` +
+ ${modal_filters} + ${search_divider} + ${result_count} +
+ ${search_results} +
+
+ `; + } else { + search_result_container = ` +
+ ${modal_filters} + ${search_divider} +
0 result(s)
+
+
No result found!
+ `; + } + + if ($(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").removeClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(search_result_container); + } else { + filter_results = []; + modal_filters = make_modal_body_filters(filters, filter_results); + + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(initial_search_body); + } +} + +/** + * Make the modal filter html + * + * @param {string[]} filters + * @param {string[]} selected_filters + * @returns string + */ +function make_modal_body_filters(filters, selected_filters = []) { + let str = ``; + + filters.forEach((val) => { + if (selected_filters.includes(val)) { + str += `${val}`; + } else { + str += `${val}`; + } + }); + + let filter_html = ` +
+ Filters: + ${str} +
+ `; + + return filter_html; +} + +/** + * Make the result component given a minisearch result data object and the value of the search input as queryString. + * To view the result object structure, refer: https://lucaong.github.io/minisearch/modules/_minisearch_.html#searchresult + * + * @param {object} result + * @param {string} querystring + * @returns string + */ +function make_search_result(result, querystring) { + let search_divider = `
`; + let display_link = + result.location.slice(Math.max(0), Math.min(50, result.location.length)) + + (result.location.length > 30 ? "..." : ""); // To cut-off the link because it messes with the overflow of the whole div + + if (result.page !== "") { + display_link += ` (${result.page})`; + } + + let textindex = new RegExp(`\\b${querystring}\\b`, "i").exec(result.text); + let text = + textindex !== null + ? result.text.slice( + Math.max(textindex.index - 100, 0), + Math.min( + textindex.index + querystring.length + 100, + result.text.length + ) + ) + : ""; // cut-off text before and after from the match + + let display_result = text.length + ? "..." + + text.replace( + new RegExp(`\\b${querystring}\\b`, "i"), // For first occurrence + '$&' + ) + + "..." + : ""; // highlights the match + + let in_code = false; + if (!["page", "section"].includes(result.category.toLowerCase())) { + in_code = true; + } + + // We encode the full url to escape some special characters which can lead to broken links + let result_div = ` + +
+
${result.title}
+
${result.category}
+
+

+ ${display_result} +

+
+ ${display_link} +
+ + ${search_divider} + `; + + return result_div; +} + +/** + * Get selected filters, remake the filter html and lastly update the search modal + */ +function get_filters() { + let ele = $(".search-filters .search-filter-selected").get(); + filter_results = ele.map((x) => $(x).text().toLowerCase()); + modal_filters = make_modal_body_filters(filters, filter_results); + update_search(filter_results); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Modal settings dialog +$(document).ready(function () { + var settings = $("#documenter-settings"); + $("#documenter-settings-button").click(function () { + settings.toggleClass("is-active"); + }); + // Close the dialog if X is clicked + $("#documenter-settings button.delete").click(function () { + settings.removeClass("is-active"); + }); + // Close dialog if ESC is pressed + $(document).keyup(function (e) { + if (e.keyCode == 27) settings.removeClass("is-active"); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +let search_modal_header = ` +
+
+

+ + + + +

+
+
+ +
+
+`; + +let initial_search_body = ` +
Type something to get started!
+`; + +let search_modal_footer = ` + +`; + +$(document.body).append( + ` + + ` +); + +document.querySelector(".docs-search-query").addEventListener("click", () => { + openModal(); +}); + +document.querySelector(".close-search-modal").addEventListener("click", () => { + closeModal(); +}); + +$(document).on("click", ".search-result-link", function () { + closeModal(); +}); + +document.addEventListener("keydown", (event) => { + if ((event.ctrlKey || event.metaKey) && event.key === "/") { + openModal(); + } else if (event.key === "Escape") { + closeModal(); + } + + return false; +}); + +// Functions to open and close a modal +function openModal() { + let searchModal = document.querySelector("#search-modal"); + + searchModal.classList.add("is-active"); + document.querySelector(".documenter-search-input").focus(); +} + +function closeModal() { + let searchModal = document.querySelector("#search-modal"); + let initial_search_body = ` +
Type something to get started!
+ `; + + searchModal.classList.remove("is-active"); + document.querySelector(".documenter-search-input").blur(); + + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".documenter-search-input").val(""); + $(".search-modal-card-body").html(initial_search_body); +} + +document + .querySelector("#search-modal .modal-background") + .addEventListener("click", () => { + closeModal(); + }); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Manages the showing and hiding of the sidebar. +$(document).ready(function () { + var sidebar = $("#documenter > .docs-sidebar"); + var sidebar_button = $("#documenter-sidebar-button"); + sidebar_button.click(function (ev) { + ev.preventDefault(); + sidebar.toggleClass("visible"); + if (sidebar.hasClass("visible")) { + // Makes sure that the current menu item is visible in the sidebar. + $("#documenter .docs-menu a.is-active").focus(); + } + }); + $("#documenter > .docs-main").bind("click", function (ev) { + if ($(ev.target).is(sidebar_button)) { + return; + } + if (sidebar.hasClass("visible")) { + sidebar.removeClass("visible"); + } + }); +}); + +// Resizes the package name / sitename in the sidebar if it is too wide. +// Inspired by: https://github.com/davatron5000/FitText.js +$(document).ready(function () { + e = $("#documenter .docs-autofit"); + function resize() { + var L = parseInt(e.css("max-width"), 10); + var L0 = e.width(); + if (L0 > L) { + var h0 = parseInt(e.css("font-size"), 10); + e.css("font-size", (L * h0) / L0); + // TODO: make sure it survives resizes? + } + } + // call once and then register events + resize(); + $(window).resize(resize); + $(window).on("orientationchange", resize); +}); + +// Scroll the navigation bar to the currently selected menu item +$(document).ready(function () { + var sidebar = $("#documenter .docs-menu").get(0); + var active = $("#documenter .docs-menu .is-active").get(0); + if (typeof active !== "undefined") { + sidebar.scrollTop = active.offsetTop - sidebar.offsetTop - 15; + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Theme picker setup +$(document).ready(function () { + // onchange callback + $("#documenter-themepicker").change(function themepick_callback(ev) { + var themename = $("#documenter-themepicker option:selected").attr("value"); + if (themename === "auto") { + // set_theme(window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'); + window.localStorage.removeItem("documenter-theme"); + } else { + // set_theme(themename); + window.localStorage.setItem("documenter-theme", themename); + } + // We re-use the global function from themeswap.js to actually do the swapping. + set_theme_from_local_storage(); + }); + + // Make sure that the themepicker displays the correct theme when the theme is retrieved + // from localStorage + if (typeof window.localStorage !== "undefined") { + var theme = window.localStorage.getItem("documenter-theme"); + if (theme !== null) { + $("#documenter-themepicker option").each(function (i, e) { + e.selected = e.value === theme; + }); + } + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// update the version selector with info from the siteinfo.js and ../versions.js files +$(document).ready(function () { + // If the version selector is disabled with DOCUMENTER_VERSION_SELECTOR_DISABLED in the + // siteinfo.js file, we just return immediately and not display the version selector. + if ( + typeof DOCUMENTER_VERSION_SELECTOR_DISABLED === "boolean" && + DOCUMENTER_VERSION_SELECTOR_DISABLED + ) { + return; + } + + var version_selector = $("#documenter .docs-version-selector"); + var version_selector_select = $("#documenter .docs-version-selector select"); + + version_selector_select.change(function (x) { + target_href = version_selector_select + .children("option:selected") + .get(0).value; + window.location.href = target_href; + }); + + // add the current version to the selector based on siteinfo.js, but only if the selector is empty + if ( + typeof DOCUMENTER_CURRENT_VERSION !== "undefined" && + $("#version-selector > option").length == 0 + ) { + var option = $( + "" + ); + version_selector_select.append(option); + } + + if (typeof DOC_VERSIONS !== "undefined") { + var existing_versions = version_selector_select.children("option"); + var existing_versions_texts = existing_versions.map(function (i, x) { + return x.text; + }); + DOC_VERSIONS.forEach(function (each) { + var version_url = documenterBaseURL + "/../" + each + "/"; + var existing_id = $.inArray(each, existing_versions_texts); + // if not already in the version selector, add it as a new option, + // otherwise update the old option with the URL and enable it + if (existing_id == -1) { + var option = $( + "" + ); + version_selector_select.append(option); + } else { + var option = existing_versions[existing_id]; + option.value = version_url; + option.disabled = false; + } + }); + } + + // only show the version selector if the selector has been populated + if (version_selector_select.children("option").length > 0) { + version_selector.toggleClass("visible"); + } +}); + +}) diff --git a/v3.13.1/assets/img/bp-message.svg b/v3.13.1/assets/img/bp-message.svg new file mode 100644 index 000000000..259b70803 --- /dev/null +++ b/v3.13.1/assets/img/bp-message.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/v3.13.1/assets/img/coin-flip.png b/v3.13.1/assets/img/coin-flip.png new file mode 100644 index 000000000..2ed0f04c0 Binary files /dev/null and b/v3.13.1/assets/img/coin-flip.png differ diff --git a/v3.13.1/assets/img/coin-flip.svg b/v3.13.1/assets/img/coin-flip.svg new file mode 100644 index 000000000..a85960c12 --- /dev/null +++ b/v3.13.1/assets/img/coin-flip.svg @@ -0,0 +1,794 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/v3.13.1/assets/img/vmp-message.svg b/v3.13.1/assets/img/vmp-message.svg new file mode 100644 index 000000000..475cd7f49 --- /dev/null +++ b/v3.13.1/assets/img/vmp-message.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/v3.13.1/assets/themes/documenter-dark.css b/v3.13.1/assets/themes/documenter-dark.css new file mode 100644 index 000000000..9f5449f4b --- /dev/null +++ b/v3.13.1/assets/themes/documenter-dark.css @@ -0,0 +1,7 @@ +html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark .pagination-next,html.theme--documenter-dark .pagination-link,html.theme--documenter-dark .pagination-ellipsis,html.theme--documenter-dark .file-cta,html.theme--documenter-dark .file-name,html.theme--documenter-dark .select select,html.theme--documenter-dark .textarea,html.theme--documenter-dark .input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input,html.theme--documenter-dark .button{-moz-appearance:none;-webkit-appearance:none;align-items:center;border:1px solid transparent;border-radius:.4em;box-shadow:none;display:inline-flex;font-size:1rem;height:2.5em;justify-content:flex-start;line-height:1.5;padding-bottom:calc(0.5em - 1px);padding-left:calc(0.75em - 1px);padding-right:calc(0.75em - 1px);padding-top:calc(0.5em - 1px);position:relative;vertical-align:top}html.theme--documenter-dark .pagination-previous:focus,html.theme--documenter-dark .pagination-next:focus,html.theme--documenter-dark .pagination-link:focus,html.theme--documenter-dark .pagination-ellipsis:focus,html.theme--documenter-dark .file-cta:focus,html.theme--documenter-dark .file-name:focus,html.theme--documenter-dark .select select:focus,html.theme--documenter-dark .textarea:focus,html.theme--documenter-dark .input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:focus,html.theme--documenter-dark .button:focus,html.theme--documenter-dark .is-focused.pagination-previous,html.theme--documenter-dark .is-focused.pagination-next,html.theme--documenter-dark .is-focused.pagination-link,html.theme--documenter-dark .is-focused.pagination-ellipsis,html.theme--documenter-dark .is-focused.file-cta,html.theme--documenter-dark .is-focused.file-name,html.theme--documenter-dark .select select.is-focused,html.theme--documenter-dark .is-focused.textarea,html.theme--documenter-dark .is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-focused.button,html.theme--documenter-dark .pagination-previous:active,html.theme--documenter-dark .pagination-next:active,html.theme--documenter-dark .pagination-link:active,html.theme--documenter-dark .pagination-ellipsis:active,html.theme--documenter-dark .file-cta:active,html.theme--documenter-dark .file-name:active,html.theme--documenter-dark .select select:active,html.theme--documenter-dark .textarea:active,html.theme--documenter-dark .input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:active,html.theme--documenter-dark .button:active,html.theme--documenter-dark .is-active.pagination-previous,html.theme--documenter-dark .is-active.pagination-next,html.theme--documenter-dark .is-active.pagination-link,html.theme--documenter-dark .is-active.pagination-ellipsis,html.theme--documenter-dark .is-active.file-cta,html.theme--documenter-dark .is-active.file-name,html.theme--documenter-dark .select select.is-active,html.theme--documenter-dark .is-active.textarea,html.theme--documenter-dark .is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active,html.theme--documenter-dark .is-active.button{outline:none}html.theme--documenter-dark .pagination-previous[disabled],html.theme--documenter-dark .pagination-next[disabled],html.theme--documenter-dark .pagination-link[disabled],html.theme--documenter-dark .pagination-ellipsis[disabled],html.theme--documenter-dark .file-cta[disabled],html.theme--documenter-dark .file-name[disabled],html.theme--documenter-dark .select select[disabled],html.theme--documenter-dark .textarea[disabled],html.theme--documenter-dark .input[disabled],html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled],html.theme--documenter-dark .button[disabled],fieldset[disabled] 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.button.is-black.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-black.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-outlined:hover,html.theme--documenter-dark .button.is-black.is-outlined.is-hovered,html.theme--documenter-dark .button.is-black.is-outlined:focus,html.theme--documenter-dark .button.is-black.is-outlined.is-focused{background-color:#0a0a0a;border-color:#0a0a0a;color:#fff}html.theme--documenter-dark .button.is-black.is-outlined.is-loading::after{border-color:transparent transparent #0a0a0a #0a0a0a !important}html.theme--documenter-dark .button.is-black.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-black.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-black.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-black.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-black.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-black.is-outlined{background-color:transparent;border-color:#0a0a0a;box-shadow:none;color:#0a0a0a}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-black.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-black.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark 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.button.is-dark,html.theme--documenter-dark .content kbd.button{background-color:#282f2f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:hover,html.theme--documenter-dark .content kbd.button:hover,html.theme--documenter-dark .button.is-dark.is-hovered,html.theme--documenter-dark .content kbd.button.is-hovered{background-color:#232829;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus,html.theme--documenter-dark .content kbd.button:focus,html.theme--documenter-dark .button.is-dark.is-focused,html.theme--documenter-dark .content kbd.button.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-dark:focus:not(:active),html.theme--documenter-dark .content kbd.button:focus:not(:active),html.theme--documenter-dark .button.is-dark.is-focused:not(:active),html.theme--documenter-dark .content kbd.button.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(40,47,47,0.25)}html.theme--documenter-dark 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kbd.button.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-dark.is-inverted[disabled],html.theme--documenter-dark .content kbd.button.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-loading::after,html.theme--documenter-dark .content kbd.button.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined,html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-outlined:hover,html.theme--documenter-dark .content kbd.button.is-outlined:hover,html.theme--documenter-dark 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kbd.button.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,html.theme--documenter-dark .content 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.button.is-dark.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #282f2f #282f2f !important}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,fieldset[disabled] html.theme--documenter-dark .content 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.button.is-warning.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-outlined{background-color:transparent;border-color:#ad8100;box-shadow:none;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-focused{background-color:#fff;color:#ad8100}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #ad8100 #ad8100 !important}html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-warning.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-warning.is-light{background-color:#fffaeb;color:#d19c00}html.theme--documenter-dark .button.is-warning.is-light:hover,html.theme--documenter-dark .button.is-warning.is-light.is-hovered{background-color:#fff7de;border-color:transparent;color:#d19c00}html.theme--documenter-dark .button.is-warning.is-light:active,html.theme--documenter-dark .button.is-warning.is-light.is-active{background-color:#fff3d1;border-color:transparent;color:#d19c00}html.theme--documenter-dark .button.is-danger{background-color:#9e1b0d;border-color:transparent;color:#fff}html.theme--documenter-dark 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.button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #9e1b0d #9e1b0d !important}html.theme--documenter-dark .button.is-danger.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-outlined{background-color:transparent;border-color:#9e1b0d;box-shadow:none;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #9e1b0d #9e1b0d !important}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-danger.is-light{background-color:#fdeeec;color:#ec311d}html.theme--documenter-dark .button.is-danger.is-light:hover,html.theme--documenter-dark .button.is-danger.is-light.is-hovered{background-color:#fce3e0;border-color:transparent;color:#ec311d}html.theme--documenter-dark .button.is-danger.is-light:active,html.theme--documenter-dark .button.is-danger.is-light.is-active{background-color:#fcd8d5;border-color:transparent;color:#ec311d}html.theme--documenter-dark .button.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--documenter-dark .button.is-small:not(.is-rounded),html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--documenter-dark .button.is-normal{font-size:1rem}html.theme--documenter-dark .button.is-medium{font-size:1.25rem}html.theme--documenter-dark .button.is-large{font-size:1.5rem}html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .button{background-color:#8c9b9d;border-color:#5e6d6f;box-shadow:none;opacity:.5}html.theme--documenter-dark .button.is-fullwidth{display:flex;width:100%}html.theme--documenter-dark .button.is-loading{color:transparent !important;pointer-events:none}html.theme--documenter-dark .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--documenter-dark .button.is-static{background-color:#282f2f;border-color:#5e6d6f;color:#dbdee0;box-shadow:none;pointer-events:none}html.theme--documenter-dark .button.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--documenter-dark .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--documenter-dark .buttons .button{margin-bottom:0.5rem}html.theme--documenter-dark .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--documenter-dark .buttons:last-child{margin-bottom:-0.5rem}html.theme--documenter-dark .buttons:not(:last-child){margin-bottom:1rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--documenter-dark .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--documenter-dark .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--documenter-dark .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--documenter-dark .buttons.has-addons .button:last-child{margin-right:0}html.theme--documenter-dark .buttons.has-addons .button:hover,html.theme--documenter-dark .buttons.has-addons .button.is-hovered{z-index:2}html.theme--documenter-dark .buttons.has-addons .button:focus,html.theme--documenter-dark .buttons.has-addons .button.is-focused,html.theme--documenter-dark .buttons.has-addons .button:active,html.theme--documenter-dark .buttons.has-addons .button.is-active,html.theme--documenter-dark .buttons.has-addons .button.is-selected{z-index:3}html.theme--documenter-dark .buttons.has-addons .button:focus:hover,html.theme--documenter-dark .buttons.has-addons .button.is-focused:hover,html.theme--documenter-dark .buttons.has-addons .button:active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--documenter-dark .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .buttons.is-centered{justify-content:center}html.theme--documenter-dark .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--documenter-dark .buttons.is-right{justify-content:flex-end}html.theme--documenter-dark .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:1rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1.25rem}}html.theme--documenter-dark .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--documenter-dark .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--documenter-dark .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--documenter-dark .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--documenter-dark .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--documenter-dark .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--documenter-dark .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--documenter-dark .content li+li{margin-top:0.25em}html.theme--documenter-dark .content p:not(:last-child),html.theme--documenter-dark .content dl:not(:last-child),html.theme--documenter-dark .content ol:not(:last-child),html.theme--documenter-dark .content ul:not(:last-child),html.theme--documenter-dark .content blockquote:not(:last-child),html.theme--documenter-dark .content pre:not(:last-child),html.theme--documenter-dark .content table:not(:last-child){margin-bottom:1em}html.theme--documenter-dark .content h1,html.theme--documenter-dark .content h2,html.theme--documenter-dark .content h3,html.theme--documenter-dark .content h4,html.theme--documenter-dark .content h5,html.theme--documenter-dark .content h6{color:#f2f2f2;font-weight:600;line-height:1.125}html.theme--documenter-dark .content h1{font-size:2em;margin-bottom:0.5em}html.theme--documenter-dark .content h1:not(:first-child){margin-top:1em}html.theme--documenter-dark .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--documenter-dark .content h2:not(:first-child){margin-top:1.1428em}html.theme--documenter-dark .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--documenter-dark .content h3:not(:first-child){margin-top:1.3333em}html.theme--documenter-dark .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--documenter-dark .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--documenter-dark .content h6{font-size:1em;margin-bottom:1em}html.theme--documenter-dark .content blockquote{background-color:#282f2f;border-left:5px solid #5e6d6f;padding:1.25em 1.5em}html.theme--documenter-dark .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ol:not([type]){list-style-type:decimal}html.theme--documenter-dark .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--documenter-dark .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--documenter-dark .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--documenter-dark .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--documenter-dark .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--documenter-dark .content ul ul ul{list-style-type:square}html.theme--documenter-dark .content dd{margin-left:2em}html.theme--documenter-dark .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--documenter-dark .content figure:not(:first-child){margin-top:2em}html.theme--documenter-dark .content figure:not(:last-child){margin-bottom:2em}html.theme--documenter-dark .content figure img{display:inline-block}html.theme--documenter-dark .content figure figcaption{font-style:italic}html.theme--documenter-dark .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--documenter-dark .content sup,html.theme--documenter-dark .content sub{font-size:75%}html.theme--documenter-dark .content table{width:100%}html.theme--documenter-dark .content table td,html.theme--documenter-dark .content table th{border:1px solid #5e6d6f;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--documenter-dark .content table th{color:#f2f2f2}html.theme--documenter-dark .content table th:not([align]){text-align:inherit}html.theme--documenter-dark .content table thead td,html.theme--documenter-dark .content table thead th{border-width:0 0 2px;color:#f2f2f2}html.theme--documenter-dark .content table tfoot td,html.theme--documenter-dark .content table tfoot th{border-width:2px 0 0;color:#f2f2f2}html.theme--documenter-dark .content table tbody tr:last-child td,html.theme--documenter-dark .content table tbody tr:last-child th{border-bottom-width:0}html.theme--documenter-dark .content .tabs li+li{margin-top:0}html.theme--documenter-dark .content.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.content{font-size:.75rem}html.theme--documenter-dark .content.is-normal{font-size:1rem}html.theme--documenter-dark .content.is-medium{font-size:1.25rem}html.theme--documenter-dark .content.is-large{font-size:1.5rem}html.theme--documenter-dark .icon{align-items:center;display:inline-flex;justify-content:center;height:1.5rem;width:1.5rem}html.theme--documenter-dark .icon.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.icon{height:1rem;width:1rem}html.theme--documenter-dark .icon.is-medium{height:2rem;width:2rem}html.theme--documenter-dark .icon.is-large{height:3rem;width:3rem}html.theme--documenter-dark .icon-text{align-items:flex-start;color:inherit;display:inline-flex;flex-wrap:wrap;line-height:1.5rem;vertical-align:top}html.theme--documenter-dark .icon-text .icon{flex-grow:0;flex-shrink:0}html.theme--documenter-dark .icon-text .icon:not(:last-child){margin-right:.25em}html.theme--documenter-dark .icon-text .icon:not(:first-child){margin-left:.25em}html.theme--documenter-dark div.icon-text{display:flex}html.theme--documenter-dark .image,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img{display:block;position:relative}html.theme--documenter-dark .image img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}html.theme--documenter-dark .image img.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:9999px}html.theme--documenter-dark .image.is-fullwidth,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-fullwidth{width:100%}html.theme--documenter-dark .image.is-square img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--documenter-dark .image.is-square .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--documenter-dark .image.is-1by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 img,html.theme--documenter-dark .image.is-1by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,html.theme--documenter-dark .image.is-5by4 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 img,html.theme--documenter-dark .image.is-5by4 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 .has-ratio,html.theme--documenter-dark .image.is-4by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 img,html.theme--documenter-dark .image.is-4by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 .has-ratio,html.theme--documenter-dark .image.is-3by2 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 img,html.theme--documenter-dark .image.is-3by2 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 .has-ratio,html.theme--documenter-dark .image.is-5by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 img,html.theme--documenter-dark .image.is-5by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,html.theme--documenter-dark .image.is-16by9 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 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+ Maintainer: @highlightjs/core-team + Website: https://highlightjs.org/ + License: see project LICENSE + Touched: 2021 +*/pre code.hljs{display:block;overflow-x:auto;padding:1em}code.hljs{padding:3px 5px}.hljs{background:#F3F3F3;color:#444}.hljs-comment{color:#697070}.hljs-tag,.hljs-punctuation{color:#444a}.hljs-tag .hljs-name,.hljs-tag .hljs-attr{color:#444}.hljs-keyword,.hljs-attribute,.hljs-selector-tag,.hljs-meta .hljs-keyword,.hljs-doctag,.hljs-name{font-weight:bold}.hljs-type,.hljs-string,.hljs-number,.hljs-selector-id,.hljs-selector-class,.hljs-quote,.hljs-template-tag,.hljs-deletion{color:#880000}.hljs-title,.hljs-section{color:#880000;font-weight:bold}.hljs-regexp,.hljs-symbol,.hljs-variable,.hljs-template-variable,.hljs-link,.hljs-selector-attr,.hljs-operator,.hljs-selector-pseudo{color:#ab5656}.hljs-literal{color:#695}.hljs-built_in,.hljs-bullet,.hljs-code,.hljs-addition{color:#397300}.hljs-meta{color:#1f7199}.hljs-meta .hljs-string{color:#38a}.hljs-emphasis{font-style:italic}.hljs-strong{font-weight:bold}.gap-4{gap:1rem} diff --git a/v3.13.1/assets/themeswap.js b/v3.13.1/assets/themeswap.js new file mode 100644 index 000000000..9f5eebe6a --- /dev/null +++ b/v3.13.1/assets/themeswap.js @@ -0,0 +1,84 @@ +// Small function to quickly swap out themes. Gets put into the tag.. +function set_theme_from_local_storage() { + // Initialize the theme to null, which means default + var theme = null; + // If the browser supports the localstorage and is not disabled then try to get the + // documenter theme + if (window.localStorage != null) { + // Get the user-picked theme from localStorage. May be `null`, which means the default + // theme. + theme = window.localStorage.getItem("documenter-theme"); + } + // Check if the users preference is for dark color scheme + var darkPreference = + window.matchMedia("(prefers-color-scheme: dark)").matches === true; + // Initialize a few variables for the loop: + // + // - active: will contain the index of the theme that should be active. Note that there + // is no guarantee that localStorage contains sane values. If `active` stays `null` + // we either could not find the theme or it is the default (primary) theme anyway. + // Either way, we then need to stick to the primary theme. + // + // - disabled: style sheets that should be disabled (i.e. all the theme style sheets + // that are not the currently active theme) + var active = null; + var disabled = []; + var primaryLightTheme = null; + var primaryDarkTheme = null; + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + // The tag of each style sheet is expected to have a data-theme-name attribute + // which must contain the name of the theme. The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // To distinguish the default (primary) theme, it needs to have the data-theme-primary + // attribute set. + if (ss.ownerNode.getAttribute("data-theme-primary") !== null) { + primaryLightTheme = themename; + } + // Check if the theme is primary dark theme so that we could store its name in darkTheme + if (ss.ownerNode.getAttribute("data-theme-primary-dark") !== null) { + primaryDarkTheme = themename; + } + // If we find a matching theme (and it's not the default), we'll set active to non-null + if (themename === theme) active = i; + // Store the style sheets of inactive themes so that we could disable them + if (themename !== theme) disabled.push(ss); + } + var activeTheme = null; + if (active !== null) { + // If we did find an active theme, we'll (1) add the theme--$(theme) class to + document.getElementsByTagName("html")[0].className = "theme--" + theme; + activeTheme = theme; + } else { + // If we did _not_ find an active theme, then we need to fall back to the primary theme + // which can either be dark or light, depending on the user's OS preference. + var activeTheme = darkPreference ? primaryDarkTheme : primaryLightTheme; + // In case it somehow happens that the relevant primary theme was not found in the + // preceding loop, we abort without doing anything. + if (activeTheme === null) { + console.error("Unable to determine primary theme."); + return; + } + // When switching to the primary light theme, then we must not have a class name + // for the tag. That's only for non-primary or the primary dark theme. + if (darkPreference) { + document.getElementsByTagName("html")[0].className = + "theme--" + activeTheme; + } else { + document.getElementsByTagName("html")[0].className = ""; + } + } + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + // The tag of each style sheet is expected to have a data-theme-name attribute + // which must contain the name of the theme. The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // we'll disable all the stylesheets, except for the active one + ss.disabled = !(themename == activeTheme); + } +} +set_theme_from_local_storage(); diff --git a/v3.13.1/assets/warner.js b/v3.13.1/assets/warner.js new file mode 100644 index 000000000..3f6f5d008 --- /dev/null +++ b/v3.13.1/assets/warner.js @@ -0,0 +1,52 @@ +function maybeAddWarning() { + // DOCUMENTER_NEWEST is defined in versions.js, DOCUMENTER_CURRENT_VERSION and DOCUMENTER_STABLE + // in siteinfo.js. + // If either of these are undefined something went horribly wrong, so we abort. + if ( + window.DOCUMENTER_NEWEST === undefined || + window.DOCUMENTER_CURRENT_VERSION === undefined || + window.DOCUMENTER_STABLE === undefined + ) { + return; + } + + // Current version is not a version number, so we can't tell if it's the newest version. Abort. + if (!/v(\d+\.)*\d+/.test(window.DOCUMENTER_CURRENT_VERSION)) { + return; + } + + // Current version is newest version, so no need to add a warning. + if (window.DOCUMENTER_NEWEST === window.DOCUMENTER_CURRENT_VERSION) { + return; + } + + // Add a noindex meta tag (unless one exists) so that search engines don't index this version of the docs. + if (document.body.querySelector('meta[name="robots"]') === null) { + const meta = document.createElement("meta"); + meta.name = "robots"; + meta.content = "noindex"; + + document.getElementsByTagName("head")[0].appendChild(meta); + } + + const div = document.createElement("div"); + div.classList.add("outdated-warning-overlay"); + const closer = document.createElement("button"); + closer.classList.add("outdated-warning-closer", "delete"); + closer.addEventListener("click", function () { + document.body.removeChild(div); + }); + const href = window.documenterBaseURL + "/../" + window.DOCUMENTER_STABLE; + div.innerHTML = + 'This documentation is not for the latest stable release, but for either the development version or an older release.
Click here to go to the documentation for the latest stable release.'; + div.appendChild(closer); + document.body.appendChild(div); +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", maybeAddWarning); +} else { + maybeAddWarning(); +} diff --git a/v3.13.1/custom/custom-addons/index.html b/v3.13.1/custom/custom-addons/index.html new file mode 100644 index 000000000..57443a5b6 --- /dev/null +++ b/v3.13.1/custom/custom-addons/index.html @@ -0,0 +1,24 @@ + +Custom addons · ReactiveMP.jl
GitHub

Custom Addons

Standard message passing schemes only pass along distributions to other nodes. However, for more advanced usage, there might be a need for passing along additional information in messages and/or marginals. One can for example think of passing along the scaling of the distribution or some information that specifies how the message or marginal was computed, i.e. which messages were used for its computation and which node was preceding it. Another use cases is saving extra debugging information inside messages themselves, e.g. what arguments have been used to compute a message.

Addons provide a solution here. Basically, addons are structures that contain extra information that are passed along the graph with messages and marginals in a tuple. These addons can be extracted using the getaddons(message/marginal) function. Its usage and operations can differ significantly for each application, yet below gives a concise overview on how to implement them on your own.

Example

Suppose that we wish to create an addon that counts the number of computations that preceded some message or marginal. This addon can be created by adding the file src/addons/count.jl and by including it in the ReactiveMP.jl file.

Step 1: Creating the addon structure

Let's start by defining our new addon structure. This might seem daunting, but basically only requires us to specify the information that we would like to collect. Just make sure that it is specified as a subtype of AbstractAddon. In our example this becomes:

struct AddonCount{T} <: AbstractAddon
+    count :: T
+end

You can add additional fields or functions for improved handling, such as get_count() or show() functions.

Step 2: Compute addon value after computing a message

As a second step we need to specify how the addon behaves when a new message is computed in a factor node. For this purpose we need to implement a specialized version of the message_mapping_addon() function. This function accepts the mapping variables of the factor node and updates the addons by extending the tuple.

In our example we could write

# This specification assumes that the default value for addon is `AddonCount(nothing)`
+function message_mapping_addon(::AddonCount{Nothing}, mapping, messages, marginals, result, addons)
+
+    # get number of operations of messages
+    message_count = 0
+    for message in messages
+        message_count += getcount(message)
+    end
+
+    # get number of operations of marginals
+    marginal_count = 0
+    for marginal in marginals
+        marginal_count += getcount(marginal)
+    end
+
+    # extend addons with AddonCount() structure
+    return AddonCount(message_count + marginal_count + 1)
+end
+

Step 3: Computing products

The goal is to update the AddonCount structure when we multiply 2 messages. As a result, we need to write a function that allows us to define this behaviour. This function is called multiply_addons and accepts 5 arguments. In our example this becomes

function multiply_addons(left_addon::AddonCount, right_addon::AddonCount, new_dist, left_dist, right_dist)
+    return AddonCount(left_addon.count + right_addon.count + 1)
+end

here we add the number of operations from the addons that are being multiplied and we add one (for the current operation). we are aware that this is likely not valid for iterative message passing schemes, but it still serves as a nice example. the left_addon and right_addon argument specify the addoncount objects that are being multiplied. corresponding to these addons, there are the distributions left_dist and right_dist, which might contain information for computing the product. the new distribution new_dist ∝ left_dist * right_dist is also passed along for potentially reusing the result of earlier computations.

More information

For more advanced information check the implementation of the log-scale or memory addons.

diff --git a/v3.13.1/custom/custom-functional-form/index.html b/v3.13.1/custom/custom-functional-form/index.html new file mode 100644 index 000000000..13c2e379b --- /dev/null +++ b/v3.13.1/custom/custom-functional-form/index.html @@ -0,0 +1,21 @@ + +Custom functional form · ReactiveMP.jl
GitHub

Custom Functional Form Specification

In a nutshell, functional form constraints defines a function that approximates the product of colliding messages and computes posterior marginal that can be used later on during the inference procedure. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. For example, if we refer to our CustomFunctionalForm as to f we can see the whole functional form constraints pipeline as:

\[q(x) = f\left(\frac{\overrightarrow{\mu}(x)\overleftarrow{\mu}(x)}{\int \overrightarrow{\mu}(x)\overleftarrow{\mu}(x) \mathrm{d}x}\right)\]

Interface

ReactiveMP.jl, however, uses some extra utility functions to define functional form constraint behaviour. Here we briefly describe all utility function. If you are only interested in the concrete example, you may directly head to the Custom Functional Form example at the end of this section.

Abstract super type

ReactiveMP.AbstractFormConstraint — Type
AbstractFormConstraint

Every functional form constraint is a subtype of AbstractFormConstraint abstract type.

Note: this is not strictly necessary, but it makes automatic dispatch easier and compatible with the CompositeFormConstraint.

See also: CompositeFormConstraint

source
ReactiveMP.CompositeFormConstraint — Type
CompositeFormConstraint

Creates a composite form constraint that applies form constraints in order. The composed form constraints must be compatible and have the exact same form_check_strategy. Any functional form constraint that defines is_point_mass_form_constraint() = true may be used only as the last element of the composition.

source

Form check strategy

Every custom functional form must implement a new method for the default_form_check_strategy function that returns either FormConstraintCheckEach or FormConstraintCheckLast.

  • FormConstraintCheckLast: q(x) = f(μ1(x) * μ2(x) * μ3(x))
  • FormConstraintCheckEach: q(x) = f(f(μ1(x) * μ2(x)) * μ3(x))
ReactiveMP.FormConstraintCheckEach — Type
FormConstraintCheckEach

This form constraint check strategy checks functional form of the messages product after each product in an equality chain. Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed after each subsequent prod function.

See also: FormConstraintCheckLast, default_form_check_strategy, constrain_form

source
ReactiveMP.FormConstraintCheckLast — Type
FormConstraintCheckEach

This form constraint check strategy checks functional form of the last messages product in the equality chain. Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed only once after all subsequenct prod functions have been executed.

See also: FormConstraintCheckLast, default_form_check_strategy, constrain_form

source

Prod constraint

Every custom functional form must implement a new method for the default_prod_constraint function that returns a proper prod_constraint object.

ReactiveMP.default_prod_constraint — Function
default_prod_constraint(form_constraint)

Returns a default prod constraint needed to apply a given form_constraint. For most form constraints this function returns ProdGeneric.

source

Constrain form, a.k.a f

The main function that a custom functional form must implement, which we referred to as f in the beginning of this section, is the constrain_form function.

Is point mass form constraint (optional)

Every custom functional form may implement a new method for the is_point_mass_form_constraint function that returns either true or false. This is an utility function that simplifes computation of the Bethe Free Energy and is not strictly necessary.

Compatibility with @constraints macro (optional)

To make custom functional form constraint compatible with the @constraints macro, it must implement a new method for the make_form_constraint function.

ReactiveMP.make_form_constraint — Function
make_form_constraint(::Type, args...; kwargs...)

Creates form constraint object based on passed type with given args and kwargs. Used to simplify form constraint specification.

As an example:

make_form_constraint(PointMass)

creates an instance of PointMassFormConstraint and 

make_form_constraint(SampleList, 5000, LeftProposal())

should create an instance of SampleListFormConstraint.

See also: AbstractFormConstraint

source

Custom Functional Form Example

In this demo we show how to build a custom functional form constraint that is compatible with the ReactiveMP.jl inference backend. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. We show a relatively simple use-case, which might not be very useful in practice, but serves as a simple step-by-step guide. Assume that we want a specific posterior marginal of some random variable in our model to have a specific Gaussian parametrisation, for example mean-precision. We can use built-in NormalMeanPrecision distribution, but we still need to define our custom functional form constraint:

using ReactiveMP, BayesBase
+
+# First we define our functional form structure with no fields
+struct MeanPrecisionFormConstraint <: AbstractFormConstraint end

Next we define the behaviour of our functional form constraint:

ReactiveMP.is_point_mass_form_constraint(::MeanPrecisionFormConstraint) = false
+ReactiveMP.default_form_check_strategy(::MeanPrecisionFormConstraint)   = FormConstraintCheckLast()
+ReactiveMP.default_prod_constraint(::MeanPrecisionFormConstraint)       = GenericProd()
+
+function ReactiveMP.constrain_form(::MeanPrecisionFormConstraint, distribution)
+    # This is quite a naive assumption, that a given `dsitribution` object has `mean` and `precision` defined
+    # However this quantities might be approximated with some other external method, e.g. Laplace approximation
+    m = mean(distribution)      # or approximate with some other method
+    p = precision(distribution) # or approximate with some other method
+    return NormalMeanPrecision(m, p)
+end
+
+function ReactiveMP.constrain_form(::MeanPrecisionFormConstraint, distribution::BayesBase.ProductOf)
+    # ProductOf is the special case, read about this type more in the corresponding documentation section
+    # of the `BayesBase` package
+    # ...
+end
diff --git a/v3.13.1/extra/contributing/index.html b/v3.13.1/extra/contributing/index.html new file mode 100644 index 000000000..23a776a43 --- /dev/null +++ b/v3.13.1/extra/contributing/index.html @@ -0,0 +1,2 @@ + +Contributing · ReactiveMP.jl
GitHub

Contribution guidelines

We welcome all possible contributors. This page details the some of the guidelines that should be followed when contributing to this package.

Reporting bugs

We track bugs using GitHub issues. We encourage you to write complete, specific, reproducible bug reports. Mention the versions of Julia and ReactiveMP for which you observe unexpected behavior. Please provide a concise description of the problem and complement it with code snippets, test cases, screenshots, tracebacks or any other information that you consider relevant. This will help us to replicate the problem and narrow the search space for solutions.

Suggesting features

We welcome new feature proposals. However, before submitting a feature request, consider a few things:

  • Does the feature require changes in the core ReactiveMP.jl code? If it doesn't (for example, you would like to add a factor node for a particular application), you can add local extensions in your script/notebook or consider making a separate repository for your extensions.
  • If you would like to add an implementation of a feature that changes a lot in the core ReactiveMP.jl code, please open an issue on GitHub and describe your proposal first. This will allow us to discuss your proposal with you before you invest your time in implementing something that may be difficult to merge later on.

Contributing code

Installing ReactiveMP

We suggest that you use the dev command from the new Julia package manager to install ReactiveMP.jl for development purposes. To work on your fork of ReactiveMP.jl, use your fork's URL address in the dev command, for example:

] dev git@github.com:your_username/ReactiveMP.jl.git

The dev command clones ReactiveMP.jl to ~/.julia/dev/ReactiveMP. All local changes to ReactiveMP code will be reflected in imported code.

Note

It is also might be useful to install Revise.jl package as it allows you to modify code and use the changes without restarting Julia.

Committing code

We use the standard GitHub Flow workflow where all contributions are added through pull requests. In order to contribute, first fork the repository, then commit your contributions to your fork, and then create a pull request on the master branch of the ReactiveMP.jl repository.

Before opening a pull request, please make sure that all tests pass without failing! All demos (can be found in /demo/ directory) and benchmarks (can be found in /benchmark/ directory) have to run without errors as well.

Style conventions

Note

ReactiveMP.jl repository contains scripts to automatically format code according to our guidelines. Use make format command to fix code style. This command overwrites files.

We use default Julia style guide. We list here a few important points and our modifications to the Julia style guide:

  • Use 4 spaces for indentation
  • Type names use UpperCamelCase. For example: AbstractFactorNode, RandomVariable, etc..
  • Function names are lowercase with underscores, when necessary. For example: activate!, randomvar, as_variable, etc..
  • Variable names and function arguments use snake_case
  • The name of a method that modifies its argument(s) must end in !

Unit tests

We use the test-driven development (TDD) methodology for ReactiveMP.jl development. The test coverage should be as complete as possible. Please make sure that you write tests for each piece of code that you want to add.

All unit tests are located in the /test/ directory. The /test/ directory follows the structure of the /src/ directory. Each test file should have following filename format: test_*.jl. Some tests are also present in jldoctest docs annotations directly in the source code. See Julia's documentation about doctests.

The tests can be evaluated by running following command in the Julia REPL:

] test ReactiveMP

In addition tests can be evaluated by running following command in the ReactiveMP root directory:

make test

Fixes to external libraries

If a bug has been discovered in an external dependencies of the ReactiveMP.jl it is the best to open an issue directly in the dependency's github repository. You use can use the fixes.jl file for hot-fixes before a new release of the broken dependecy is available.

Makefile

ReactiveMP.jl uses Makefile for most common operations:

  • make help: Shows help snippet
  • make test: Run tests, supports extra arguments
    • make test test_args="distributions:normal_mean_variance" would run tests only from distributions/test_normal_mean_variance.jl
    • make test test_args="distributions:normal_mean_variance models:lgssm" would run tests both from distributions/test_normal_mean_variance.jl and models/test_lgssm.jl
  • make docs: Compile documentation
  • make benchmark: Run simple benchmark
  • make lint: Check codestyle
  • make format: Check and fix codestyle
diff --git a/v3.13.1/extra/extensions/index.html b/v3.13.1/extra/extensions/index.html new file mode 100644 index 000000000..217cd8be9 --- /dev/null +++ b/v3.13.1/extra/extensions/index.html @@ -0,0 +1,2 @@ + +Extensions and interaction with the Julia ecosystem · ReactiveMP.jl
GitHub

Extensions and interaction with the Julia ecosystem

ReactiveMP.jl exports extra functionality if other Julia packages are loaded in the same environment.

Optimisers.jl

The Optimizers.jl package defines many standard gradient-based optimisation rules, and tools for applying them to deeply nested models. The optimizers defined in the Optimziers.jl are compatible with the CVI approximation method.

DiffResults.jl (loaded automatically with the ForwardDiff.jl)

The DiffResults.jl provides the DiffResult type, which can be passed to in-place differentiation methods instead of an output buffer. If loaded in the current Julia session enables faster derivatives with the ForwardDiffGrad option in the CVI approximation method (in the Gaussian case).

diff --git a/v3.13.1/index.html b/v3.13.1/index.html new file mode 100644 index 000000000..ce24ee5c7 --- /dev/null +++ b/v3.13.1/index.html @@ -0,0 +1,2 @@ + +Introduction · ReactiveMP.jl
GitHub

ReactiveMP.jl

Julia package for reactive message passing Bayesian inference engine on a factor graph.

Note

This package exports only an inference engine, for the full ecosystem with convenient model and constraints specification we refer user to the RxInfer.jl package and its documentation.

Examples and tutorials

Tutorials and examples are available in the RxInfer documentation.

Table of Contents

Index

diff --git a/v3.13.1/lib/algebra/common/index.html b/v3.13.1/lib/algebra/common/index.html new file mode 100644 index 000000000..2b3963a67 --- /dev/null +++ b/v3.13.1/lib/algebra/common/index.html @@ -0,0 +1,2 @@ + +Algebra utils · ReactiveMP.jl
GitHub

Algebra common utilities

diageye

ReactiveMP.diageye — Function
diageye(::Type{T}, n::Int)

An alias for the Matrix{T}(I, n, n). Returns a matrix of size n x n with ones (of type T) on the diagonal and zeros everywhere else.

source
diageye(n::Int)

An alias for the Matrix{Float64}(I, n, n). Returns a matrix of size n x n with ones (of type Float64) on the diagonal and zeros everywhere else.

source
diff --git a/v3.13.1/lib/helpers/index.html b/v3.13.1/lib/helpers/index.html new file mode 100644 index 000000000..396705f38 --- /dev/null +++ b/v3.13.1/lib/helpers/index.html @@ -0,0 +1,10 @@ + +Helper utils · ReactiveMP.jl
GitHub

Helper utilities

ReactiveMP implements various structures/functions/methods as "helper" structures that might be useful in various contexts.

SkipIndexIterator

ReactiveMP.SkipIndexIterator — Type
SkipIndexIterator

A special type of iterator that simply iterates over internal iterator, but skips index skip.

Arguments

  • iterator: internal iterator
  • skip: index to skip (integer)

See also: skipindex

source
ReactiveMP.skipindex — Function
skipindex(iterator, skip)

Creation operator for SkipIndexIterator.

julia> s = ReactiveMP.skipindex(1:3, 2)
+2-element ReactiveMP.SkipIndexIterator{Int64, UnitRange{Int64}}:
+ 1
+ 3
+
+julia> collect(s)
+2-element Vector{Int64}:
+ 1
+ 3

See also: SkipIndexIterator

source

FunctionalIndex

ReactiveMP.FunctionalIndex — Type
FunctionalIndex

A special type of an index that represents a function that can be used only in pair with a collection. An example of a FunctionalIndex can be firstindex or lastindex, but more complex use cases are possible too, e.g. firstindex + 1. Important part of the implementation is that the resulting structure is isbitstype(...) = true, that allows to store it in parametric type as valtype.

One use case for this structure is to dispatch on and to replace begin or end (or more complex use cases, e.g. begin + 1) markers in constraints specification language.

source
diff --git a/v3.13.1/lib/message/index.html b/v3.13.1/lib/message/index.html new file mode 100644 index 000000000..39fb4bcb6 --- /dev/null +++ b/v3.13.1/lib/message/index.html @@ -0,0 +1,20 @@ + +Messages · ReactiveMP.jl
GitHub

Messages implementation

In our message passing framework one of the most important concepts is the message (wow!). Messages flow along edges of a factor graph and hold information about the part of the graph that it originates from. Usually this information is in a form of a probability distribution. Two common messages are belief propagation messages and variational messages, with are computed differently as shown below.

Abstract message type

Both belief propagation and variational messages are subtypes of a AbstractMessage supertype.

Belief-Propagation (or Sum-Product) message

message Belief propagation message

Variational message

message Variational message with structured factorisation q(x, y)q(z) assumption

Message type

All messages are encoded with the type Message.

ReactiveMP.Message — Type
Message{D, A} <: AbstractMessage

Message structure encodes a Belief Propagation message, which holds some data that usually a probability distribution, but can also be an arbitrary object. Message acts as a proxy structure to data object and proxies most of the statistical functions, e.g. mean, mode, cov etc.

Arguments

  • data::D: message always holds some data object associated with it
  • is_clamped::Bool, specifies if this message is clamped
  • is_initial::Bool, specifies if this message is initial
  • addons::A, specifies the addons of the message

Example

julia> distribution = Gamma(10.0, 2.0)
+Distributions.Gamma{Float64}(α=10.0, θ=2.0)
+
+julia> message = Message(distribution, false, true, nothing)
+Message(Distributions.Gamma{Float64}(α=10.0, θ=2.0))
+
+julia> mean(message) 
+20.0
+
+julia> getdata(message)
+Distributions.Gamma{Float64}(α=10.0, θ=2.0)
+
+julia> is_clamped(message)
+false
+
+julia> is_initial(message)
+true
+

See also: AbstractMessage, ReactiveMP.materialize!

source

From an implementation point a view the Message structure does nothing but hold some data object and redirects most of the statistical related functions to that data object. However, this object is used extensively in Julia's multiple dispatch. Our implementation also uses extra is_initial and is_clamped fields to determine if product of two messages results in is_initial or is_clamped posterior marginal. The final field contains the addons. These contain additional information on top of the functional form of the distribution, such as its scaling or computation history.

distribution = NormalMeanPrecision(0.0, 1.0)
+message      = Message(distribution, false, true, nothing)
Message(ExponentialFamily.NormalMeanPrecision{Float64}(μ=0.0, w=1.0))
mean(message), precision(message)
(0.0, 1.0)
logpdf(message, 1.0)
-1.4189385332046727
is_clamped(message), is_initial(message)
(false, true)

The user should not really interact with Message structure while working with ReactiveMP unless doing some advanced inference procedures that involve prediction.

diff --git a/v3.13.1/lib/methods/index.html b/v3.13.1/lib/methods/index.html new file mode 100644 index 000000000..44f6ef380 --- /dev/null +++ b/v3.13.1/lib/methods/index.html @@ -0,0 +1,197 @@ + +Exported methods · ReactiveMP.jl
GitHub

Using methods from ReactiveMP

In the Julia programming language (in contrast to Python for example) the most common way of loading a module is:

using ReactiveMP

A nice explanation about how modules/packages work in Julia can be found in the official documentation.

In a nutshell, Julia automatically resolves all name collisions and there is no a lot of benefit of importing specific names, e.g.:

import ReactiveMP: mean

One of the reasons for that is that Julia uses multiple-dispatch capabilities to merge names automatically and will indicate (with a warning) if something went wrong or names have unresolvable collisions on types. As a small example of this feature consider the following small import example:

import ReactiveMP: mean as mean_from_reactivemp
+import Distributions: mean as mean_from_distributions
+import BayesBase: mean as mean_from_bayes_base
mean_from_reactivemp === mean_from_distributions
true
mean_from_reactivemp === mean_from_bayes_base
true

Even though we import mean function from different packages they actually refer to the same object. Worth noting that this is not always the case - Julia will print a warning in case it finds unresolvable conflicts and usage of such functions will be disallowed unless user import them specifically. Read more about this in the section of the Julia's documentation.

# It is easier to let Julia resolve names automatically
+# Julia will not overwrite `mean` that is coming from both packages
+using ReactiveMP, Distributions, BayesBase, ExponentialFamily
mean(Normal(0.0, 1.0)) # `Normal` is an object from `Distributions.jl`
0.0
mean(NormalMeanVariance(0.0, 1.0)) # `NormalMeanVariance` is an object from `ReactiveMP.jl`
0.0

List of available methods

Below you can find a list of exported methods from ReactiveMP.jl. All methods (even private) can be always accessed with ReactiveMP. prefix, e.g ReactiveMP.mean.

Note

Some exported names are (for legacy reasons) intended for private usage only. As a result some of these methods do not have a proper associated documentation with them. We constantly improve ReactiveMP.jl library and continue to add better documentation for many exported methods, but a small portion of these methods could be removed from this list in the future.

foreach(println, names(ReactiveMP))
@average_energy
+@call_marginalrule
+@call_rule
+@logscale
+@marginalrule
+@node
+@rule
+AND
+AR
+ARMeta
+ARsafe
+ARunsafe
+AbstractApproximationMethod
+AbstractFormConstraint
+AbstractMessage
+AbstractPipelineStage
+AbstractVariable
+Adam
+AdditiveCouplingLayer
+AddonDebug
+AddonLogScale
+AddonMemory
+AsyncPipelineStage
+Autoregressive
+AverageEnergy
+BIFM
+BIFMHelper
+BIFMMeta
+CTMeta
+CTransition
+CVI
+CompanionMatrix
+CompanionMatrixTransposed
+CompiledFlowModel
+CompositeFormConstraint
+CompositePipelineStage
+ConstVariable
+ConstraintsSpecification
+ContinuousTransition
+ContinuousTransitionMeta
+CustomProdStrategy
+DataVariable
+DefaultConstraints
+DefaultMeta
+DeltaFn
+DeltaFnNode
+DeltaMeta
+Deterministic
+DifferentialEntropy
+DiscontinuePipelineStage
+EmptyPipelineStage
+FactorBoundFreeEnergy
+FactorNodeCreationOptions
+FactorNodesCollection
+Flow
+FlowMeta
+FlowModel
+FoldLeftProdStrategy
+FoldRightProdStrategy
+FormConstraintCheckEach
+FormConstraintCheckLast
+FormConstraintCheckPickDefault
+ForwardDiffGrad
+FullFactorisation
+GCV
+GCVMetadata
+GammaMixture
+GammaMixtureNode
+GaussHermiteCubature
+GaussLaguerreQuadrature
+GaussianMixture
+GaussianMixtureNode
+HalfNormal
+IMPLY
+ImportanceSamplingApproximation
+IncludeAll
+InputLayer
+InverseWishart
+LaplaceApproximation
+Linearization
+LoggerPipelineStage
+Marginal
+Marginalisation
+MeanField
+Message
+MetaSpecification
+Mixture
+MixtureNode
+MomentMatching
+MvNormalMeanScalePrecision
+NOT
+NormalMixture
+NormalMixtureNode
+OR
+PermutationLayer
+PermutationMatrix
+PlanarFlow
+Poisson
+Probit
+ProbitMeta
+ProdCVI
+RadialFlow
+RandomVariable
+RandomVariableCreationOptions
+ReactiveMP
+SkipClamped
+SkipClampedAndInitial
+SkipInitial
+SoftDot
+StandardBasisVector
+Stochastic
+Transition
+UT
+Uninformative
+Unscented
+UnscentedTransform
+UnspecifiedConstraints
+UnspecifiedFormConstraint
+UnspecifiedMeta
+VariableBoundEntropy
+VariablesCollection
+VariationalMessage
+apply_pipeline_stage
+approximation_name
+approximation_short_name
+as_marginal
+as_message
+as_variable
+compile
+constrain_form
+constvar
+datavar
+default_form_check_strategy
+default_prod_constraint
+degree
+diageye
+dot
+factorisation
+functional_dependencies
+functionalform
+getapproximation
+getconst
+getconstant
+getdata
+getlayers
+getlogscale
+getmarginal
+getmarginals
+getmemory
+getmodel
+getnode_ids
+getnodes
+getprediction
+getpredictions
+getrandom
+getvardict
+ghcubature
+hasconstvar
+hasdatavar
+hasrandomvar
+huge
+interfaces
+is_clamped
+is_initial
+is_marginalisation
+is_moment_matching
+is_point_mass_form_constraint
+isconnected
+isdeterministic
+isstochastic
+laplace
+localmarginalnames
+localmarginals
+make_form_constraint
+make_node
+marginalrule
+metadata
+name
+nr_params
+randomvar
+rule
+score
+sdtype
+setmarginal!
+setmarginals!
+setmessage!
+setmessages!
+skipindex
+softdot
+srcubature
+tiny
+update!
diff --git a/v3.13.1/lib/nodes/flow/index.html b/v3.13.1/lib/nodes/flow/index.html new file mode 100644 index 000000000..925c1eefb --- /dev/null +++ b/v3.13.1/lib/nodes/flow/index.html @@ -0,0 +1,9 @@ + +Flow · ReactiveMP.jl
GitHub

Flow node

See also Flow tutorial for a comprehensive guide on using flows in ReactiveMP.

ReactiveMP.PlanarFlow — Type

The PlanarFlow function is defined as

\[f({\bf{x}}) = {\bf{x}} + {\bf{u}} \tanh({\bf{w}}^\top {\bf{x}} + b)\]

with input and output dimension $D$. Here ${\bf{x}}\in \mathbb{R}^D$ represents the input of the function. Furthermore ${\bf{u}}\in \mathbb{R}^D$, ${\bf{w}}\in \mathbb{R}^D$ and $b\in\mathbb{R}$ represent the parameters of the function. The function contracts and expands the input space.

This function has been introduced in:

Rezende, Danilo, and Shakir Mohamed. "Variational inference with normalizing flows." International conference on machine learning. PMLR, 2015.

source
ReactiveMP.RadialFlow — Type

The RadialFlow function is defined as

\[f({\bf{x}}) = {\bf{x}} + \frac{\beta({bf{z}} - {\bf{z}}_0)}{\alpha + |{\bf{z}} - {\bf{z}}_0|}\]

with input and output dimension $D$. Here ${\bf{x}}\in \mathbb{R}^D$ represents the input of the function. Furthermore ${\bf{z}}_0\in \mathbb{R}^D$, $\alpha\in \mathbb{R}$ and $\beta\in\mathbb{R}$ represent the parameters of the function. The function contracts and expands the input space.

This function has been introduced in:

Rezende, Danilo, and Shakir Mohamed. "Variational inference with normalizing flows." International conference on machine learning. PMLR, 2015.

source
ReactiveMP.FlowModel — Type

The FlowModel structure is the most generic type of Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimensionality and a tuple of layers and can be constructed as FlowModel( dim, (layer1, layer2, ...) ).

Note: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.

source
ReactiveMP.CompiledFlowModel — Type

The CompiledFlowModel structure is the most generic type of compiled Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimension and a tuple of compiled layers. Do not manually create a CompiledFlowModel! Instead create a FlowModel first and compile it with compile(model::FlowModel). This will make sure that all layers/mappings are configured with the proper dimensionality and with randomly sampled parameters. Alternatively, if you would like to pass your own parameters, call compile(model::FlowModel, params::Vector).

Note: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.

source
ReactiveMP.compile — Function

compile() compiles a model by setting its parameters. It randomly sets parameter values in the layers and flows such that inference in the model can be obtained.

Input arguments

  • model::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.

Return arguments

  • ::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.
source

compile(model::FlowModel, params::Vector) lets you initialize a model model with a vector of parameters params.

Input arguments

  • model::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.
  • params::Vector - a vector of parameters with which the model should be compiled.

Return arguments

  • ::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.
source
ReactiveMP.AdditiveCouplingLayer — Type

The additive coupling layer specifies an invertible function ${\bf{y}} = g({\bf{x}})$ following the specific structure (for the mapping $g: \mathbb{R}^2 \rightarrow \mathbb{R}^2$):

\[ \begin{align} + y_1 &= x_1 \\ + y_2 &= x_2 + f(x_1) + \end{align}\]

where $f(\cdot)$ denotes an arbitrary function with mapping $f: \mathbb{R} \rightarrow \mathbb{R}$. This function can be chosen arbitrarily complex. Non-linear functions (neural networks) are often chosen to model complex relationships. From the definition of the model, invertibility can be easily achieved as

\[ \begin{align} + x_1 &= y_1 \\ + x_2 &= y_2 - f(y_1) + \end{align}\]

The current implementation only allows for the mapping $g: \mathbb{R}^2 \rightarrow \mathbb{R}^2$, although this layer can be generalized for arbitrary input dimensions.

AdditiveCouplingLayer(f <: AbstractCouplingFlow) creates the layer structure with function f.

Example

f = PlanarFlow()
+layer = AdditiveCouplingLayer(f)

This layer structure has been introduced in:

Dinh, Laurent, David Krueger, and Yoshua Bengio. "Nice: Non-linear independent components estimation." arXiv preprint arXiv:1410.8516 (2014).

source
ReactiveMP.PermutationLayer — Type

The permutation layer specifies an invertible mapping ${\bf{y}} = g({\bf{x}}) = P{\bf{x}}$ where $P$ is a permutation matrix.

source
ReactiveMP.FlowMeta — Type

The FlowMeta structure contains the meta data of the Flow factor node. More specifically, it contains the model of the Flow factor node. The FlowMeta structure can be constructed as FlowMeta(model). Make sure that the flow model has been compiled.

The FlowMeta structure is required for the Flow factor node and can be included with the Flow node as: y ~ Flow(x) where { meta = FlowMeta(...) }

source
diff --git a/v3.13.1/lib/nodes/nodes/index.html b/v3.13.1/lib/nodes/nodes/index.html new file mode 100644 index 000000000..134696b92 --- /dev/null +++ b/v3.13.1/lib/nodes/nodes/index.html @@ -0,0 +1,37 @@ + +Overview · ReactiveMP.jl
GitHub

Nodes implementation

In the message passing framework, one of the most important concepts is a factor node. A factor node represents a local function in a factorised representation of a generative model.

Note

To quickly check the list of all available factor nodes that can be used in the model specification language, call ?make_node or Base.doc(make_node).

Adding a custom node

ReactiveMP.jl exports the @node macro that allows for quick definition of a factor node with a fixed number of edges. The interface is the following:

struct MyNewCustomNode end
+
+@node MyNewCustomNode   Stochastic         [ x, y, z ]
+#     ^^^^^^^^^^^^^^^   ^^^^^^^^^^^^^      ^^^^^^^^^^^
+#     Node's tag/name   Node's type        A fixed set of edges
+#                       Another possible   The very first edge (in this example `x`) is considered
+#                       value is           to be the output of the node
+#                       `Deterministic`

This expression registers a new node that can be used with the inference engine. Note howeve, that the @node macro does not generate any message passing update rules. These must be defined using the @rule macro.

Node types

We distinguish different types of factor nodes in order to have better control over Bethe Free Energy computation. Each factor node has either the Deterministic or Stochastic functional form type.

For example the + node has the Deterministic type:

plus_node = make_node(+)
+
+println("Is `+` node deterministic: ", isdeterministic(plus_node))
+println("Is `+` node stochastic: ", isstochastic(plus_node))
Is `+` node deterministic: true
+Is `+` node stochastic: false

On the other hand, the Bernoulli node has the Stochastic type:

bernoulli_node = make_node(Bernoulli)
+
+println("Is `Bernoulli` node deterministic: ", isdeterministic(bernoulli_node))
+println("Is `Bernoulli` node stochastic: ", isstochastic(bernoulli_node))
Is `Bernoulli` node deterministic: false
+Is `Bernoulli` node stochastic: true

To get an actual instance of the type object we use sdtype function:

println("sdtype() of `+` node is ", sdtype(plus_node))
+println("sdtype() of `Bernoulli` node is ", sdtype(bernoulli_node))
sdtype() of `+` node is Deterministic()
+sdtype() of `Bernoulli` node is Stochastic()

Node functional dependencies pipeline

The generic implementation of factor nodes in ReactiveMP supports custom functional dependency pipelines. Briefly, the functional dependencies pipeline defines what dependencies are need to compute a single message. As an example, consider the belief-propagation message update equation for a factor node $f$ with three edges: $x$, $y$ and $z$:

\[\mu(x) = \int \mu(y) \mu(z) f(x, y, z) \mathrm{d}y \mathrm{d}z\]

Here we see that in the standard setting for the belief-propagation message out of edge $x$, we need only messages from the edges $y$ and $z$. In contrast, consider the variational message update rule equation with mean-field assumption:

\[\mu(x) = \exp \int q(y) q(z) \log f(x, y, z) \mathrm{d}y \mathrm{d}z\]

We see that in this setting, we do not need messages $\mu(y)$ and $\mu(z)$, but only the marginals $q(y)$ and $q(z)$. The purpose of a functional dependencies pipeline is to determine functional dependencies (a set of messages or marginals) that are needed to compute a single message. By default, ReactiveMP.jl uses so-called DefaultFunctionalDependencies that correctly implements belief-propagation and variational message passing schemes (including both mean-field and structured factorisations). The full list of built-in pipelines is presented below:

ReactiveMP.RequireMessageFunctionalDependencies — Type
RequireMessageFunctionalDependencies(indices::Tuple, start_with::Tuple)

The same as DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the inbound message on the this edge.

Arguments

  • indices::Tuple, tuple of integers, which indicates what edges should require inbound messages
  • start_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial inbound messages for edges in indices

Note: start_with uses setmessage! mechanism, hence, it can be visible by other listeners on the same edge. Explicit call to setmessage! overwrites whatever has been passed in start_with.

@model macro accepts a simplified construction of this pipeline:

@model function some_model()
+    # ...
+    y ~ NormalMeanVariance(x, τ) where {
+        pipeline = RequireMessage(x = vague(NormalMeanPrecision),     τ)
+                                  # ^^^                               ^^^
+                                  # request 'inbound' for 'x'         we may do the same for 'τ',
+                                  # and initialise with `vague(...)`  but here we skip initialisation
+    }
+    # ...
+end

Deprecation warning: RequireInboundFunctionalDependencies has been deprecated in favor of RequireMessageFunctionalDependencies.

See also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMarginalFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies

source
ReactiveMP.RequireMarginalFunctionalDependencies — Type
RequireMarginalFunctionalDependencies(indices::Tuple, start_with::Tuple)

Similar to DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the posterior marginal on that edge.

Arguments

  • indices::Tuple, tuple of integers, which indicates what edges should require their own marginals
  • start_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial marginal for edges in indices

Note: start_with uses the setmarginal! mechanism, hence it can be visible to other listeners on the same edge. Explicit calls to setmarginal! overwrites whatever has been passed in start_with.

@model macro accepts a simplified construction of this pipeline:

@model function some_model()
+    # ...
+    y ~ NormalMeanVariance(x, τ) where {
+        pipeline = RequireMarginal(x = vague(NormalMeanPrecision),     τ)
+                                   # ^^^                               ^^^
+                                   # request 'marginal' for 'x'        we may do the same for 'τ',
+                                   # and initialise with `vague(...)`  but here we skip initialisation
+    }
+    # ...
+end

Note: The simplified construction in @model macro syntax is only available in GraphPPL.jl of version >2.2.0.

See also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMessageFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies

source
ReactiveMP.RequireEverythingFunctionalDependencies — Type

RequireEverythingFunctionalDependencies

This pipeline specifies that in order to compute a message of some edge update rules request everything that is available locally. This includes all inbound messages (including on the same edge) and marginals over all local edge-clusters (this may or may not include marginals on single edges, depends on the local factorisation constraint).

See also: DefaultFunctionalDependencies, RequireMessageFunctionalDependencies, RequireMarginalFunctionalDependencies

source

Node traits

Each factor node has to define the ReactiveMP.as_node_functional_form trait function and to specify a ReactiveMP.ValidNodeFunctionalForm singleton as a return object. By default ReactiveMP.as_node_functional_form returns ReactiveMP.UndefinedNodeFunctionalForm. Objects that do not specify this property correctly cannot be used in model specification.

Note

@node macro does that automatically

diff --git a/v3.13.1/lib/rules/rules/index.html b/v3.13.1/lib/rules/rules/index.html new file mode 100644 index 000000000..9c7fc3c2a --- /dev/null +++ b/v3.13.1/lib/rules/rules/index.html @@ -0,0 +1,38 @@ + +Message update rules · ReactiveMP.jl
GitHub

Rules implementation

Message update rules

ReactiveMP.rule — Function
rule(fform, on, vconstraint, mnames, messages, qnames, marginals, meta, __node)

This function is used to compute an outbound message for a given node

Arguments

  • fform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)
  • on: Outbound interface's tag for which a message has to be computed, e.g. ::Val{:out} or ::Val{:μ}
  • vconstraint: Variable constraints for an outbound interface, e.g. Marginalisation or MomentMatching
  • mnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • messages: Tuple of message of the same length as mnames used to compute an outbound message
  • qnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • marginals: Tuple of marginals of the same length as qnames used to compute an outbound message
  • meta: Extra meta information
  • addons: Extra addons information
  • __node: Node reference

For all available rules, see ReactiveMP.print_rules_table().

See also: @rule, marginalrule, @marginalrule

source
ReactiveMP.@rule — Macro
@rule NodeType(:Edge, Constraint) (Arguments..., [ meta::MetaType ]) = begin
+    # rule body
+    return ...
+end

The @rule macro help to define new methods for the rule function. It works particularly well in combination with the @node macro. It has a specific structure, which must specify:

  • NodeType: must be a valid Julia type. If some attempt to define a rule for a Julia function (for example +), use typeof(+)
  • Edge: edge label, usually edge labels are defined with the @node macro
  • Constrain: DEPRECATED, please just use the Marginalisation label
  • Arguments: defines a list of the input arguments for the rule
    • m_* prefix indicates that the argument is of type Message from the edge *
    • q_* prefix indicates that the argument is of type Marginal on the edge *
  • Meta::MetaType - optionally, a user can specify a Meta object of type MetaType. This can be useful is some attempts to try different rules with different approximation methods or if the rule itself requires some temporary storage or cache. The default meta is nothing.

Here are various examples of the @rule macro usage:

  1. Belief-Propagation (or Sum-Product) message update rule for the NormalMeanVariance node toward the :μ edge with the Marginalisation constraint. Input arguments are m_out and m_v, which are the messages from the corresponding edges out and v and have the type PointMass.
@rule NormalMeanVariance(:μ, Marginalisation) (m_out::PointMass, m_v::PointMass) = NormalMeanVariance(mean(m_out), mean(m_v))
  1. Mean-field message update rule for the NormalMeanVariance node towards the :μ edge with the Marginalisation constraint. Input arguments are q_out and q_v, which are the marginals on the corresponding edges out and v of type Any.
@rule NormalMeanVariance(:μ, Marginalisation) (q_out::Any, q_v::Any) = NormalMeanVariance(mean(q_out), mean(q_v))
  1. Structured Variational message update rule for the NormalMeanVariance node towards the :out edge with the Marginalisation constraint. Input arguments are m_μ, which is a message from the μ edge of type UnivariateNormalDistributionsFamily, and q_v, which is a marginal on the v edge of type Any.
@rule NormalMeanVariance(:out, Marginalisation) (m_μ::UnivariateNormalDistributionsFamily, q_v::Any) = begin
+    m_μ_mean, m_μ_cov = mean_cov(m_μ)
+    return NormalMeanVariance(m_μ_mean, m_μ_cov + mean(q_v))
+end

See also: rule, marginalrule, [@marginalrule], @call_rule

source
ReactiveMP.@call_rule — Macro
@call_rule NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])

The @call_rule macro helps to call the rule method with an easier syntax. The structure of the macro is almost the same as in the @rule macro, but there is no begin ... end block, but instead each argument must have a specified value with the = operator.

The @call_rule accepts optional list of options before the functional form specification, for example:

@call_rule [ return_addons = true ] NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])

The list of available options is:

  • return_addons - forces the @call_rule to return the tuple of (result, addons)

See also: @rule, rule, @call_marginalrule

source
ReactiveMP.print_rules_table — Function
Prints a table of all message passing update rules in ReactiveMP.
+
+Use `Markdown.parse` on the output of this function to get a prettified table.
source

Marginal update rules

ReactiveMP.marginalrule — Function
marginalrule(fform, on, mnames, messages, qnames, marginals, meta, __node)

This function is used to compute a local joint marginal for a given node

Arguments

  • fform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)
  • on: Local joint marginal tag , e.g. ::Val{ :mean_precision } or ::Val{ :out_mean_precision }
  • mnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • messages: Tuple of message of the same length as mnames used to compute an outbound message
  • qnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • marginals: Tuple of marginals of the same length as qnames used to compute an outbound message
  • meta: Extra meta information
  • __node: Node reference

See also: rule, @rule @marginalrule

source
ReactiveMP.@marginalrule — Macro
@marginalrule NodeType(:Cluster) (Arguments..., [ meta::MetaType ]) = begin
+    # rule body
+    return ...
+end

The @marginalrule macro help to define new methods for the marginalrule function. It works particularly well in combination with the @node macro. It has a specific structure, which must specify:

  • NodeType: must be a valid Julia type. If some attempt to define a rule for a Julia function (for example +), use typeof(+)
  • Cluster: edge cluster that contains joined edge labels with the _ symbol. Usually edge labels are defined with the @node macro
  • Arguments: defines a list of the input arguments for the rule
    • m_* prefix indicates that the argument is of type Message from the edge *
    • q_* prefix indicates that the argument is of type Marginal on the edge *
  • Meta::MetaType - optionally, a user can specify a Meta object of type MetaType. This can be useful is some attempts to try different rules with different approximation methods or if the rule itself requires some temporary storage or cache. The default meta is nothing.

The @marginalrule can return a NamedTuple in the return statement. This would indicate some variables in the joint marginal for the Cluster are independent and the joint itself is factorised. For example if some attempts to compute a marginal for the q(x, y) it is possible to return (x = ..., y = ...) as the result of the computation to indicate that q(x, y) = q(x)q(y).

Here are various examples of the @marginalrule macro usage:

  1. Marginal computation rule around the NormalMeanPrecision node for the q(out, μ). The rule accepts arguments m_out and m_μ, which are the messages

from the out and μ edges respectively, and q_τ which is the marginal on the edge τ.

@marginalrule NormalMeanPrecision(:out_μ) (m_out::UnivariateNormalDistributionsFamily, m_μ::UnivariateNormalDistributionsFamily, q_τ::Any) = begin
+    xi_out, W_out = weightedmean_precision(m_out)
+    xi_μ, W_μ     = weightedmean_precision(m_μ)
+
+    W_bar = mean(q_τ)
+
+    W  = [W_out+W_bar -W_bar; -W_bar W_μ+W_bar]
+    xi = [xi_out; xi_μ]
+
+    return MvNormalWeightedMeanPrecision(xi, W)
+end
  1. Marginal computation rule around the NormalMeanPrecision node for the q(out, μ). The rule accepts arguments m_out and m_μ, which are the messages from the

out and μ edges respectively, and q_τ which is the marginal on the edge τ. In this example the result of the computation is a NamedTuple

@marginalrule NormalMeanPrecision(:out_μ) (m_out::PointMass, m_μ::UnivariateNormalDistributionsFamily, q_τ::Any) = begin
+    return (out = m_out, μ = prod(ClosedProd(), NormalMeanPrecision(mean(m_out), mean(q_τ)), m_μ))
+end
source
ReactiveMP.@call_marginalrule — Macro
@call_marginalrule NodeType(:edge) (argument1 = value1, argument2 = value2, ..., [ meta = ... ])

The @call_marginalrule macro helps to call the marginalrule method with an easier syntax. The structure of the macro is almost the same as in the @marginalrule macro, but there is no begin ... end block, but instead each argument must have a specified value with the = operator.

See also: @marginalrule, marginalrule, @call_rule

source

Testing utilities for the update rules

ReactiveMP.@test_rules — Macro
@test_rules [options] rule [ test_entries... ]

The @test_rules macro generates test cases for message update rules for probabilistic programming models that follow the "message passing" paradigm. It takes a rule specification as input and generates a set of tests based on that specification. This macro is provided by ReactiveMP.

Note: The Test module must be imported explicitly. The @test_rules macro tries to use the @test macro, which must be defined globally.

Arguments

The macro takes three arguments:

  • options: An optional argument that specifies the options for the test generation process. See below for details.
  • rule: A rule specification in the same format as the @rule macro, e.g. Beta(:out, Marginalisation) or NormalMeanVariance(:μ, Marginalisation).
  • test_entries: An array of named tuples (input = ..., output = ...). The input entry has the same format as the input for the @rule macro. The output entry specifies the expected output.

Options

The following options are available:

  • check_type_promotion: By default, this option is set to false. If set to true, the macro generates an extensive list of extra tests that aim to check the correct type promotion within the tests. For example, if all inputs are of type Float32, then the expected output should also be of type Float32. See the paramfloattype and convert_paramfloattype functions for details.
  • atol: Sets the desired accuracy for the tests. The tests use the custom_rule_isapprox function from ReactiveMP to check if outputs are approximately the same. This argument can be either a single number or an array of key => value pairs.
  • extra_float_types: A set of extra float types to be used in the check_type_promotion tests. This argument has no effect if check_type_promotion is set to false.

The default values for the atol option are:

  • Float32: 1e-4
  • Float64: 1e-6
  • BigFloat: 1e-8

Examples


+@test_rules [check_type_promotion = true] Beta(:out, Marginalisation) [
+    (input = (m_a = PointMass(1.0), m_b = PointMass(2.0)), output = Beta(1.0, 2.0)),
+    (input = (m_a = PointMass(2.0), m_b = PointMass(2.0)), output = Beta(2.0, 2.0)),
+    (input = (m_a = PointMass(3.0), m_b = PointMass(3.0)), output = Beta(3.0, 3.0))
+]
+
+@test_rules [check_type_promotion = true] Beta(:out, Marginalisation) [
+    (input = (q_a = PointMass(1.0), q_b = PointMass(2.0)), output = Beta(1.0, 2.0)),
+    (input = (q_a = PointMass(2.0), q_b = PointMass(2.0)), output = Beta(2.0, 2.0)),
+    (input = (q_a = PointMass(3.0), q_b = PointMass(3.0)), output = Beta(3.0, 3.0))
+]

See also: ReactiveMP.@test_marginalrules

source

Table of available update rules

using ReactiveMP, Markdown
+Markdown.parse(ReactiveMP.print_rules_table())
NodeOutputInputsMeta
*inμ(A) :: BayesBase.PointMass{<:LinearAlgebra.UniformScaling}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
*inμ(A) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: BayesBase.PointMass{<:Real}
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecision
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: ExponentialFamily.MvNormalMeanPrecision
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: ExponentialFamily.MvNormalMeanCovariance
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*inμ(out) :: Distributions.UnivariateDistributionUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: Distributions.UnivariateDistribution
*inμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*inμ(out) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass
*inμ(out) :: ExponentialFamily.GammaDistributionsFamilyUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:Real}
*inμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractMatrix}
*inμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractVector}
*inμ(out) :: FUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:Real}
*inμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractMatrix}
*inμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractVector}
*outμ(A) :: Distributions.UnivariateDistributionUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Distributions.UnivariateDistribution
*outμ(A) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*outμ(A) :: BayesBase.PointMass{<:LinearAlgebra.UniformScaling}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
*outμ(A) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.MvNormalWeightedMeanPrecision
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.MvNormalMeanPrecision
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.MvNormalMeanCovariance
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*outμ(A) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.GammaDistributionsFamily
*outμ(A) :: ExponentialFamily.GammaDistributionsFamilyUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*outμ(A) :: BayesBase.PointMass{<:AbstractMatrix}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: F
*outμ(A) :: FUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractMatrix}
*outμ(A) :: BayesBase.PointMass{<:AbstractVector}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*outμ(A) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractVector}
*Aμ(out) :: Distributions.UnivariateDistributionUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Distributions.UnivariateDistribution
*Aμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*Aμ(out) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass
*Aμ(out) :: ExponentialFamily.GammaDistributionsFamilyUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*Aμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractMatrix}
*Aμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractVector}
*Aμ(out) :: FUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*Aμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractMatrix}
*Aμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractVector}
+in2μ(out) :: Any
μ(in1) :: Any
-in2μ(out) :: Any
μ(in1) :: Any
-in1μ(out) :: Any
μ(in2) :: Any
-outμ(in1) :: Any
μ(in2) :: Any
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision
+in1μ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T1, M, P} where {M<:AbstractVector{T1}, P<:AbstractMatrix{T1}}Nothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T1, M, P} where {M<:AbstractVector{T1}, P<:AbstractMatrix{T1}}Nothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T2, M, P} where {M<:AbstractVector{T2}, P<:AbstractMatrix{T2}}
+in1μ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+in1μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+in1μ(out) :: ExponentialFamily.MvNormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalMeanPrecision
+in1μ(out) :: ExponentialFamily.NormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.NormalMeanPrecision
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+in1μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision
+outμ(in1) :: ExponentialFamily.MvNormalWeightedMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T1, M, P} where {M<:AbstractVector{T1}, P<:AbstractMatrix{T1}}Nothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T2, M, P} where {M<:AbstractVector{T2}, P<:AbstractMatrix{T2}}
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalMeanPrecision
+outμ(in1) :: ExponentialFamily.MvNormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.NormalMeanPrecision
+outμ(in1) :: ExponentialFamily.NormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Distributions.DistributionNothing
μ(in2) :: Distributions.Distribution
dotin2μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in1) :: BayesBase.PointMass
dotin1μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in2) :: BayesBase.PointMass
dotoutμ(in1) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in2) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
dotoutμ(in1) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in2) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVariancevq(out_μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
ExponentialFamily.NormalMeanVariancevq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.NormalMeanVariancevμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
ExponentialFamily.NormalMeanVariancevμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(μ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVariancevμ(out) :: BayesBase.PointMassNothing
μ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
ExponentialFamily.NormalMeanVarianceμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceμμ(out) :: BayesBase.PointMassNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
q(v) :: Any
ExponentialFamily.NormalMeanVarianceμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμμ(out) :: BayesBase.PointMassNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμq(out) :: BayesBase.PointMassNothing
q(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμq(out) :: AnyNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: BayesBase.PointMassNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutq(μ) :: BayesBase.PointMassNothing
q(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceoutq(μ) :: AnyNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: BayesBase.PointMassNothing
μ(v) :: BayesBase.PointMass
SoftDotγq(y) :: AnyNothing
q(θ) :: Any
q(x) :: Any
SoftDotθq(y) :: AnyNothing
q(x) :: Any
q(γ) :: Any
SoftDotxq(y) :: AnyNothing
q(θ) :: Any
q(γ) :: Any
SoftDotyq(θ) :: AnyNothing
q(x) :: Any
q(γ) :: Any
ANDoutμ(in1) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
ORoutμ(in1) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
ANDin2μ(out) :: Distributions.BernoulliNothing
μ(in1) :: Distributions.Bernoulli
ORin2μ(out) :: Distributions.BernoulliNothing
μ(in1) :: Distributions.Bernoulli
ANDin1μ(out) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
ORin1μ(out) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
Mixtureswitchμ(out) :: AnyNothing
μ(inputs) :: ManyOf{<:Tuple{Vararg{Any, N}}}
Mixtureoutμ(inputs) :: ManyOf{<:Tuple{Vararg{Any, N}}}Nothing
q(switch) :: BayesBase.PointMass
Mixtureoutμ(switch) :: AnyNothing
μ(inputs) :: ManyOf{<:Tuple{Vararg{Any, N}}}
Mixtureinputsμ(out) :: AnyNothing
q(switch) :: BayesBase.PointMass
Mixtureinputsμ(out) :: AnyNothing
μ(switch) :: Any
Transitioninq(out) :: BayesBase.PointMassNothing
q(a) :: BayesBase.PointMass
Transitioninq(out) :: AnyNothing
q(a) :: ExponentialFamily.MatrixDirichlet
Transitioninμ(out) :: Distributions.Categorical{P} where P<:Real
q(a) :: BayesBase.PointMass
Transitioninμ(out) :: Distributions.Categorical{P} where P<:RealNothing
q(a) :: ExponentialFamily.MatrixDirichlet
Transitioninμ(out) :: Distributions.Categorical{P} where P<:RealNothing
μ(a) :: BayesBase.PointMass
Transitionoutμ(in) :: Distributions.DiscreteNonParametric
q(a) :: BayesBase.PointMass
Transitionoutμ(in) :: Distributions.Categorical{P} where P<:RealNothing
q(a) :: Distributions.Distribution{Distributions.Matrixvariate, Distributions.Continuous}
Transitionoutq(in) :: Distributions.Categorical{P} where P<:RealNothing
q(a) :: Any
Transitionoutq(in) :: BayesBase.PointMassNothing
q(a) :: BayesBase.PointMass
Transitionoutμ(in) :: Distributions.Categorical{P} where P<:RealNothing
μ(a) :: BayesBase.PointMass
Transitionaq(out_in) :: BayesBase.ContingencyNothing
Transitionaq(out) :: AnyNothing
q(in) :: Distributions.Categorical{P} where P<:Real
Distributions.Wishartoutq(ν) :: AnyNothing
q(S) :: Any
InverseWishartoutq(ν) :: AnyNothing
q(S) :: Any
Distributions.Wishartoutμ(ν) :: BayesBase.PointMassNothing
q(S) :: Any
InverseWishartoutμ(ν) :: BayesBase.PointMassNothing
q(S) :: Any
Distributions.Wishartoutμ(S) :: BayesBase.PointMassNothing
q(ν) :: Any
InverseWishartoutμ(S) :: BayesBase.PointMassNothing
q(ν) :: Any
Distributions.Wishartoutμ(ν) :: BayesBase.PointMassNothing
μ(S) :: BayesBase.PointMass
InverseWishartoutμ(ν) :: BayesBase.PointMassNothing
μ(S) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionτq(out_μ) :: AnyNothing
ExponentialFamily.NormalMeanPrecisionτq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμq(out) :: BayesBase.PointMassNothing
q(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμq(out) :: AnyNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutq(μ) :: BayesBase.PointMassNothing
q(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionoutq(μ) :: AnyNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
μ(τ) :: BayesBase.PointMass
Distributions.InverseGammaoutq(α) :: AnyNothing
q(θ) :: Any
Distributions.Gammaoutq(α) :: AnyNothing
q(θ) :: Any
Distributions.InverseGammaoutμ(α) :: BayesBase.PointMassNothing
μ(θ) :: BayesBase.PointMass
Distributions.Gammaoutμ(α) :: BayesBase.PointMassNothing
μ(θ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceΣq(out_μ) :: AnyNothing
ExponentialFamily.MvNormalMeanCovarianceΣq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: BayesBase.PointMassNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutq(μ) :: BayesBase.PointMassNothing
q(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceoutq(μ) :: AnyNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: BayesBase.PointMassNothing
μ(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: BayesBase.PointMassNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceμq(out) :: BayesBase.PointMassNothing
q(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceμq(out) :: AnyNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: BayesBase.PointMassNothing
μ(Σ) :: BayesBase.PointMass
MvNormalMeanScalePrecisionγq(out_μ) :: AnyNothing
MvNormalMeanScalePrecisionγq(out) :: AnyNothing
q(μ) :: Any
MvNormalMeanScalePrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(γ) :: Any
MvNormalMeanScalePrecisionμq(out) :: AnyNothing
q(γ) :: Any
MvNormalMeanScalePrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(γ) :: Any
MvNormalMeanScalePrecisionoutq(μ) :: AnyNothing
q(γ) :: Any
NormalMixture{N}outq(switch) :: AnyNothing
q(m) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(p) :: ManyOf{<:Tuple{Vararg{Any, N}}}
NormalMixturepq(out) :: AnyNothing
q(switch) :: Any
q(m) :: Any
NormalMixturemq(out) :: AnyNothing
q(switch) :: Any
q(p) :: Any
NormalMixture{N}switchq(out) :: AnyNothing
q(m) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(p) :: ManyOf{<:Tuple{Vararg{Any, N}}}
Distributions.Bernoullipq(out) :: Distributions.Categorical{P} where P<:RealNothing
Distributions.Bernoullipq(out) :: Distributions.BernoulliNothing
Distributions.Bernoullipq(out) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realpq(out) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realpq(out) :: AnyNothing
Distributions.Bernoullipμ(out) :: BayesBase.PointMassNothing
Distributions.Bernoullioutq(p) :: AnyNothing
Distributions.Bernoullioutq(p) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutq(p) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutq(p) :: Distributions.DirichletNothing
Distributions.Bernoullioutμ(p) :: BayesBase.PointMassNothing
Distributions.Bernoullioutμ(p) :: Distributions.BetaNothing
Distributions.Categorical{P} where P<:Realoutμ(p) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutμ(p) :: Distributions.DirichletNothing
GCVyq(x) :: AnyUnion{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
GCVyμ(x) :: Union{ReactiveMP.ExponentialLinearQuadratic, ExponentialFamily.UnivariateNormalDistributionsFamily, ExponentialFamily.UnivariateGaussianDistributionsFamily}Union{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
GCVzq(y) :: AnyGCVMetadata
q(x) :: Any
q(κ) :: Any
q(ω) :: Any
GCVzq(y_x) :: AnyGCVMetadata
q(κ) :: Any
q(ω) :: Any
GCVκq(y) :: AnyGCVMetadata
q(x) :: Any
q(z) :: Any
q(ω) :: Any
GCVκq(y_x) :: AnyGCVMetadata
q(z) :: Any
q(ω) :: Any
GCVωq(y) :: AnyGCVMetadata
q(x) :: Any
q(z) :: Any
q(κ) :: Any
GCVωq(y_x) :: AnyGCVMetadata
q(z) :: Any
q(κ) :: Any
GCVxq(y) :: AnyUnion{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
GCVxμ(y) :: Union{ReactiveMP.ExponentialLinearQuadratic, ExponentialFamily.UnivariateNormalDistributionsFamily, ExponentialFamily.UnivariateGaussianDistributionsFamily}Union{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
ExponentialFamily.MatrixDirichletoutq(a) :: BayesBase.PointMassNothing
Distributions.Dirichletoutq(a) :: BayesBase.PointMass{<:AbstractVector}Nothing
ExponentialFamily.MatrixDirichletoutμ(a) :: BayesBase.PointMassNothing
Distributions.Dirichletoutμ(a) :: BayesBase.PointMass{<:AbstractVector}Nothing
ContinuousTransitionWq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
ContinuousTransitionaq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
q(W) :: Any
ContinuousTransitionxμ(y) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
q(W) :: Any
ContinuousTransitionyμ(x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
q(W) :: Any
BIFMHelperoutq(in) :: AnyNothing
Probitoutq(in) :: BayesBase.PointMassUnion{Nothing, ProbitMeta}
BIFMHelperinμ(out) :: AnyNothing
Probitinμ(out) :: Union{BayesBase.PointMass, Distributions.Bernoulli}Union{Nothing, ProbitMeta}
NOTinμ(out) :: Distributions.BernoulliNothing
ExponentialFamily.GammaShapeRateαq(out) :: Distributions.GammaNothing
q(β) :: Distributions.Gamma
ExponentialFamily.GammaShapeRateoutq(α) :: BayesBase.PointMassNothing
q(β) :: BayesBase.PointMass
ExponentialFamily.GammaShapeRateoutμ(α) :: BayesBase.PointMassNothing
μ(β) :: BayesBase.PointMass
Poissonoutq(l) :: ExponentialFamily.GammaDistributionsFamilyNothing
Poissonoutμ(l) :: BayesBase.PointMassNothing
Poissonlq(out) :: AnyNothing
Poissonlμ(out) :: BayesBase.PointMassNothing
Probitoutμ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, ProbitMeta}
Probitoutμ(in) :: BayesBase.PointMassUnion{Nothing, ProbitMeta}
NOToutμ(in) :: Distributions.BernoulliNothing
Probitinμ(out) :: Union{BayesBase.PointMass, Distributions.Bernoulli}Union{Nothing, ProbitMeta}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
Probitinμ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, ProbitMeta}
q(out) :: BayesBase.PointMass
Probitinq(out) :: BayesBase.PointMassUnion{Nothing, ProbitMeta}
HalfNormaloutq(v) :: BayesBase.PointMassNothing
GammaMixture{N}outq(switch) :: AnyNothing
q(a) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(b) :: ExponentialFamily.GammaDistributionsFamily
GammaMixturebq(out) :: AnyNothing
q(switch) :: Any
q(a) :: Any
GammaMixtureaq(out) :: AnyNothing
q(switch) :: Any
q(b) :: ExponentialFamily.GammaDistributionsFamily
GammaMixture{N}switchq(out) :: AnyNothing
q(a) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(b) :: ExponentialFamily.GammaDistributionsFamily
ExponentialFamily.MvNormalWeightedMeanPrecisionoutq(ξ) :: AnyNothing
q(Λ) :: Any
ExponentialFamily.MvNormalWeightedMeanPrecisionoutμ(ξ) :: BayesBase.PointMassNothing
μ(Λ) :: BayesBase.PointMass
ARyq(x) :: AnyARMeta
q(θ) :: Any
q(γ) :: Any
ARyμ(x) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TARMeta
q(θ) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
q(γ) :: Any
ARθq(y) :: AnyARMeta
q(x) :: Any
q(γ) :: Any
ARθq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TARMeta
q(γ) :: Any
ARxq(y) :: AnyARMeta
q(θ) :: Any
q(γ) :: Any
ARxμ(y) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TARMeta
q(θ) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
q(γ) :: Any
ARγq(y) :: AnyARMeta
q(x) :: Any
q(θ) :: Any
ARγq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TARMeta
q(θ) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
ExponentialFamily.MvNormalMeanPrecisionΛq(out_μ) :: AnyNothing
ExponentialFamily.MvNormalMeanPrecisionΛq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Distributions.Wishart
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutq(μ) :: BayesBase.PointMassNothing
q(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionoutq(μ) :: AnyNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Distributions.Wishart
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionμq(out) :: BayesBase.PointMassNothing
q(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionμq(out) :: AnyNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
μ(Λ) :: BayesBase.PointMass
Distributions.Betaoutq(a) :: BayesBase.PointMassNothing
q(b) :: BayesBase.PointMass
Distributions.Uniformoutq(a) :: BayesBase.PointMassNothing
q(b) :: BayesBase.PointMass
Distributions.Betaoutμ(a) :: BayesBase.PointMassNothing
μ(b) :: BayesBase.PointMass
Distributions.Uniformoutμ(a) :: BayesBase.PointMassNothing
μ(b) :: BayesBase.PointMass
DeltaFnoutq(ins) :: BayesBase.FactorizedJoint{P}DeltaMeta{M}
DeltaFnoutq(ins) :: BayesBase.FactorizedJointDeltaMeta{M}
DeltaFnoutμ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M}
DeltaFnoutμ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M}
DeltaFninμ(in) :: AnyDeltaMeta{M}
q(ins) :: BayesBase.FactorizedJoint
DeltaFninμ(in) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, Nothing}
q(ins) :: ExponentialFamily.JointNormal
DeltaFninμ(in) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
q(ins) :: ExponentialFamily.JointNormal
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Nothing
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Nothing
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
Distributions.Uniformoutμ(b) :: BayesBase.PointMassNothing
q(a) :: BayesBase.PointMass
Distributions.Uniformoutμ(a) :: BayesBase.PointMassNothing
q(b) :: BayesBase.PointMass
Flowinμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TFlowMeta{M, <:Unscented}
Flowinμ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecisionFlowMeta{M, <:Linearization}
Flowinμ(out) :: ExponentialFamily.MvNormalMeanPrecisionFlowMeta{M, <:Linearization}
Flowinμ(out) :: ExponentialFamily.MvNormalMeanCovarianceFlowMeta{M, <:Linearization}
Flowoutμ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TFlowMeta{M, <:Unscented}
Flowoutμ(in) :: ExponentialFamily.MvNormalWeightedMeanPrecisionFlowMeta{M, <:Linearization}
Flowoutμ(in) :: ExponentialFamily.MvNormalMeanPrecisionFlowMeta{M, <:Linearization}
Flowoutμ(in) :: ExponentialFamily.MvNormalMeanCovarianceFlowMeta{M, <:Linearization}
BIFMzprevμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
μ(znext) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
BIFMznextμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
μ(zprev) :: BayesBase.TerminalProdArgument{<:Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T}
BIFMoutμ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(zprev) :: BayesBase.TerminalProdArgument{<:Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T}
μ(znext) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
BIFMinμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(zprev) :: BayesBase.TerminalProdArgument{<:Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T}
μ(znext) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
outμ(in1) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
in2μ(out) :: Distributions.BernoulliNothing
μ(in1) :: Distributions.Bernoulli
in1μ(out) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
+
diff --git a/v3.13.1/search_index.js b/v3.13.1/search_index.js new file mode 100644 index 000000000..8d55bd0c1 --- /dev/null +++ b/v3.13.1/search_index.js @@ -0,0 +1,3 @@ +var documenterSearchIndex = {"docs": +[{"location":"lib/helpers/#lib-helpers","page":"Helper utils","title":"Helper utilities","text":"","category":"section"},{"location":"lib/helpers/","page":"Helper utils","title":"Helper utils","text":"ReactiveMP implements various structures/functions/methods as \"helper\" structures that might be useful in various contexts.","category":"page"},{"location":"lib/helpers/#lib-helpers-skip-index-iterator","page":"Helper utils","title":"SkipIndexIterator","text":"","category":"section"},{"location":"lib/helpers/","page":"Helper utils","title":"Helper utils","text":"ReactiveMP.SkipIndexIterator\nReactiveMP.skipindex","category":"page"},{"location":"lib/helpers/#ReactiveMP.SkipIndexIterator","page":"Helper utils","title":"ReactiveMP.SkipIndexIterator","text":"SkipIndexIterator\n\nA special type of iterator that simply iterates over internal iterator, but skips index skip.\n\nArguments\n\niterator: internal iterator\nskip: index to skip (integer)\n\nSee also: skipindex\n\n\n\n\n\n","category":"type"},{"location":"lib/helpers/#ReactiveMP.skipindex","page":"Helper utils","title":"ReactiveMP.skipindex","text":"skipindex(iterator, skip)\n\nCreation operator for SkipIndexIterator.\n\njulia> s = ReactiveMP.skipindex(1:3, 2)\n2-element ReactiveMP.SkipIndexIterator{Int64, UnitRange{Int64}}:\n 1\n 3\n\njulia> collect(s)\n2-element Vector{Int64}:\n 1\n 3\n\nSee also: SkipIndexIterator\n\n\n\n\n\n","category":"function"},{"location":"lib/helpers/#lib-helpers-functional-index","page":"Helper utils","title":"FunctionalIndex","text":"","category":"section"},{"location":"lib/helpers/","page":"Helper utils","title":"Helper utils","text":"ReactiveMP.FunctionalIndex","category":"page"},{"location":"lib/helpers/#ReactiveMP.FunctionalIndex","page":"Helper utils","title":"ReactiveMP.FunctionalIndex","text":"FunctionalIndex\n\nA special type of an index that represents a function that can be used only in pair with a collection. An example of a FunctionalIndex can be firstindex or lastindex, but more complex use cases are possible too, e.g. firstindex + 1. Important part of the implementation is that the resulting structure is isbitstype(...) = true, that allows to store it in parametric type as valtype.\n\nOne use case for this structure is to dispatch on and to replace begin or end (or more complex use cases, e.g. begin + 1) markers in constraints specification language.\n\n\n\n\n\n","category":"type"},{"location":"lib/rules/rules/#lib-rules","page":"Message update rules","title":"Rules implementation","text":"","category":"section"},{"location":"lib/rules/rules/#lib-message-rules","page":"Message update rules","title":"Message update rules","text":"","category":"section"},{"location":"lib/rules/rules/","page":"Message update rules","title":"Message update rules","text":"rule\n@rule\n@call_rule\nReactiveMP.print_rules_table","category":"page"},{"location":"lib/rules/rules/#ReactiveMP.rule","page":"Message update rules","title":"ReactiveMP.rule","text":"rule(fform, on, vconstraint, mnames, messages, qnames, marginals, meta, __node)\n\nThis function is used to compute an outbound message for a given node\n\nArguments\n\nfform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)\non: Outbound interface's tag for which a message has to be computed, e.g. ::Val{:out} or ::Val{:μ}\nvconstraint: Variable constraints for an outbound interface, e.g. Marginalisation or MomentMatching\nmnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }\nmessages: Tuple of message of the same length as mnames used to compute an outbound message\nqnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }\nmarginals: Tuple of marginals of the same length as qnames used to compute an outbound message\nmeta: Extra meta information\naddons: Extra addons information\n__node: Node reference\n\nFor all available rules, see ReactiveMP.print_rules_table().\n\nSee also: @rule, marginalrule, @marginalrule\n\n\n\n\n\n","category":"function"},{"location":"lib/rules/rules/#ReactiveMP.@rule","page":"Message update rules","title":"ReactiveMP.@rule","text":"@rule NodeType(:Edge, Constraint) (Arguments..., [ meta::MetaType ]) = begin\n # rule body\n return ...\nend\n\nThe @rule macro help to define new methods for the rule function. It works particularly well in combination with the @node macro. It has a specific structure, which must specify:\n\nNodeType: must be a valid Julia type. If some attempt to define a rule for a Julia function (for example +), use typeof(+)\nEdge: edge label, usually edge labels are defined with the @node macro\nConstrain: DEPRECATED, please just use the Marginalisation label\nArguments: defines a list of the input arguments for the rule\nm_* prefix indicates that the argument is of type Message from the edge *\nq_* prefix indicates that the argument is of type Marginal on the edge *\nMeta::MetaType - optionally, a user can specify a Meta object of type MetaType. This can be useful is some attempts to try different rules with different approximation methods or if the rule itself requires some temporary storage or cache. The default meta is nothing.\n\nHere are various examples of the @rule macro usage:\n\nBelief-Propagation (or Sum-Product) message update rule for the NormalMeanVariance node toward the :μ edge with the Marginalisation constraint. Input arguments are m_out and m_v, which are the messages from the corresponding edges out and v and have the type PointMass.\n\n@rule NormalMeanVariance(:μ, Marginalisation) (m_out::PointMass, m_v::PointMass) = NormalMeanVariance(mean(m_out), mean(m_v))\n\nMean-field message update rule for the NormalMeanVariance node towards the :μ edge with the Marginalisation constraint. Input arguments are q_out and q_v, which are the marginals on the corresponding edges out and v of type Any.\n\n@rule NormalMeanVariance(:μ, Marginalisation) (q_out::Any, q_v::Any) = NormalMeanVariance(mean(q_out), mean(q_v))\n\nStructured Variational message update rule for the NormalMeanVariance node towards the :out edge with the Marginalisation constraint. Input arguments are m_μ, which is a message from the μ edge of type UnivariateNormalDistributionsFamily, and q_v, which is a marginal on the v edge of type Any.\n\n@rule NormalMeanVariance(:out, Marginalisation) (m_μ::UnivariateNormalDistributionsFamily, q_v::Any) = begin\n m_μ_mean, m_μ_cov = mean_cov(m_μ)\n return NormalMeanVariance(m_μ_mean, m_μ_cov + mean(q_v))\nend\n\nSee also: rule, marginalrule, [@marginalrule], @call_rule\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#ReactiveMP.@call_rule","page":"Message update rules","title":"ReactiveMP.@call_rule","text":"@call_rule NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])\n\nThe @call_rule macro helps to call the rule method with an easier syntax. The structure of the macro is almost the same as in the @rule macro, but there is no begin ... end block, but instead each argument must have a specified value with the = operator.\n\nThe @call_rule accepts optional list of options before the functional form specification, for example:\n\n@call_rule [ return_addons = true ] NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])\n\nThe list of available options is:\n\nreturn_addons - forces the @call_rule to return the tuple of (result, addons)\n\nSee also: @rule, rule, @call_marginalrule\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#ReactiveMP.print_rules_table","page":"Message update rules","title":"ReactiveMP.print_rules_table","text":"Prints a table of all message passing update rules in ReactiveMP.\n\nUse `Markdown.parse` on the output of this function to get a prettified table.\n\n\n\n\n\n","category":"function"},{"location":"lib/rules/rules/#lib-marginal-rules","page":"Message update rules","title":"Marginal update rules","text":"","category":"section"},{"location":"lib/rules/rules/","page":"Message update rules","title":"Message update rules","text":"marginalrule\n@marginalrule\n@call_marginalrule","category":"page"},{"location":"lib/rules/rules/#ReactiveMP.marginalrule","page":"Message update rules","title":"ReactiveMP.marginalrule","text":"marginalrule(fform, on, mnames, messages, qnames, marginals, meta, __node)\n\nThis function is used to compute a local joint marginal for a given node\n\nArguments\n\nfform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)\non: Local joint marginal tag , e.g. ::Val{ :mean_precision } or ::Val{ :out_mean_precision }\nmnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }\nmessages: Tuple of message of the same length as mnames used to compute an outbound message\nqnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }\nmarginals: Tuple of marginals of the same length as qnames used to compute an outbound message\nmeta: Extra meta information\n__node: Node reference\n\nSee also: rule, @rule @marginalrule\n\n\n\n\n\n","category":"function"},{"location":"lib/rules/rules/#ReactiveMP.@marginalrule","page":"Message update rules","title":"ReactiveMP.@marginalrule","text":"@marginalrule NodeType(:Cluster) (Arguments..., [ meta::MetaType ]) = begin\n # rule body\n return ...\nend\n\nThe @marginalrule macro help to define new methods for the marginalrule function. It works particularly well in combination with the @node macro. It has a specific structure, which must specify:\n\nNodeType: must be a valid Julia type. If some attempt to define a rule for a Julia function (for example +), use typeof(+)\nCluster: edge cluster that contains joined edge labels with the _ symbol. Usually edge labels are defined with the @node macro\nArguments: defines a list of the input arguments for the rule\nm_* prefix indicates that the argument is of type Message from the edge *\nq_* prefix indicates that the argument is of type Marginal on the edge *\nMeta::MetaType - optionally, a user can specify a Meta object of type MetaType. This can be useful is some attempts to try different rules with different approximation methods or if the rule itself requires some temporary storage or cache. The default meta is nothing.\n\nThe @marginalrule can return a NamedTuple in the return statement. This would indicate some variables in the joint marginal for the Cluster are independent and the joint itself is factorised. For example if some attempts to compute a marginal for the q(x, y) it is possible to return (x = ..., y = ...) as the result of the computation to indicate that q(x, y) = q(x)q(y).\n\nHere are various examples of the @marginalrule macro usage:\n\nMarginal computation rule around the NormalMeanPrecision node for the q(out, μ). The rule accepts arguments m_out and m_μ, which are the messages \n\nfrom the out and μ edges respectively, and q_τ which is the marginal on the edge τ.\n\n@marginalrule NormalMeanPrecision(:out_μ) (m_out::UnivariateNormalDistributionsFamily, m_μ::UnivariateNormalDistributionsFamily, q_τ::Any) = begin\n xi_out, W_out = weightedmean_precision(m_out)\n xi_μ, W_μ = weightedmean_precision(m_μ)\n\n W_bar = mean(q_τ)\n\n W = [W_out+W_bar -W_bar; -W_bar W_μ+W_bar]\n xi = [xi_out; xi_μ]\n\n return MvNormalWeightedMeanPrecision(xi, W)\nend\n\nMarginal computation rule around the NormalMeanPrecision node for the q(out, μ). The rule accepts arguments m_out and m_μ, which are the messages from the \n\nout and μ edges respectively, and q_τ which is the marginal on the edge τ. In this example the result of the computation is a NamedTuple\n\n@marginalrule NormalMeanPrecision(:out_μ) (m_out::PointMass, m_μ::UnivariateNormalDistributionsFamily, q_τ::Any) = begin\n return (out = m_out, μ = prod(ClosedProd(), NormalMeanPrecision(mean(m_out), mean(q_τ)), m_μ))\nend\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#ReactiveMP.@call_marginalrule","page":"Message update rules","title":"ReactiveMP.@call_marginalrule","text":"@call_marginalrule NodeType(:edge) (argument1 = value1, argument2 = value2, ..., [ meta = ... ])\n\nThe @call_marginalrule macro helps to call the marginalrule method with an easier syntax. The structure of the macro is almost the same as in the @marginalrule macro, but there is no begin ... end block, but instead each argument must have a specified value with the = operator.\n\nSee also: @marginalrule, marginalrule, @call_rule\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#lib-rules-tests","page":"Message update rules","title":"Testing utilities for the update rules","text":"","category":"section"},{"location":"lib/rules/rules/","page":"Message update rules","title":"Message update rules","text":"ReactiveMP.@test_rules\nReactiveMP.@test_marginalrules","category":"page"},{"location":"lib/rules/rules/#ReactiveMP.@test_rules","page":"Message update rules","title":"ReactiveMP.@test_rules","text":"@test_rules [options] rule [ test_entries... ]\n\nThe @test_rules macro generates test cases for message update rules for probabilistic programming models that follow the \"message passing\" paradigm. It takes a rule specification as input and generates a set of tests based on that specification. This macro is provided by ReactiveMP.\n\nNote: The Test module must be imported explicitly. The @test_rules macro tries to use the @test macro, which must be defined globally.\n\nArguments\n\nThe macro takes three arguments:\n\noptions: An optional argument that specifies the options for the test generation process. See below for details.\nrule: A rule specification in the same format as the @rule macro, e.g. Beta(:out, Marginalisation) or NormalMeanVariance(:μ, Marginalisation).\ntest_entries: An array of named tuples (input = ..., output = ...). The input entry has the same format as the input for the @rule macro. The output entry specifies the expected output.\n\nOptions\n\nThe following options are available:\n\ncheck_type_promotion: By default, this option is set to false. If set to true, the macro generates an extensive list of extra tests that aim to check the correct type promotion within the tests. For example, if all inputs are of type Float32, then the expected output should also be of type Float32. See the paramfloattype and convert_paramfloattype functions for details.\natol: Sets the desired accuracy for the tests. The tests use the custom_rule_isapprox function from ReactiveMP to check if outputs are approximately the same. This argument can be either a single number or an array of key => value pairs.\nextra_float_types: A set of extra float types to be used in the check_type_promotion tests. This argument has no effect if check_type_promotion is set to false.\n\nThe default values for the atol option are:\n\nFloat32: 1e-4\nFloat64: 1e-6\nBigFloat: 1e-8\n\nExamples\n\n\n@test_rules [check_type_promotion = true] Beta(:out, Marginalisation) [\n (input = (m_a = PointMass(1.0), m_b = PointMass(2.0)), output = Beta(1.0, 2.0)),\n (input = (m_a = PointMass(2.0), m_b = PointMass(2.0)), output = Beta(2.0, 2.0)),\n (input = (m_a = PointMass(3.0), m_b = PointMass(3.0)), output = Beta(3.0, 3.0))\n]\n\n@test_rules [check_type_promotion = true] Beta(:out, Marginalisation) [\n (input = (q_a = PointMass(1.0), q_b = PointMass(2.0)), output = Beta(1.0, 2.0)),\n (input = (q_a = PointMass(2.0), q_b = PointMass(2.0)), output = Beta(2.0, 2.0)),\n (input = (q_a = PointMass(3.0), q_b = PointMass(3.0)), output = Beta(3.0, 3.0))\n]\n\nSee also: ReactiveMP.@test_marginalrules\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#ReactiveMP.@test_marginalrules","page":"Message update rules","title":"ReactiveMP.@test_marginalrules","text":"@test_marginalrules [options] rule [ test_entries... ]\n\nEffectively the same as @test_rules, but for marginal computation rules. See the documentation for @test_rules for more info.\n\nSee also: ReactiveMP.@test_rules\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#lib-rules-table","page":"Message update rules","title":"Table of available update rules","text":"","category":"section"},{"location":"lib/rules/rules/","page":"Message update rules","title":"Message update rules","text":"using ReactiveMP, Markdown\nMarkdown.parse(ReactiveMP.print_rules_table())","category":"page"},{"location":"lib/message/#lib-message","page":"Messages","title":"Messages implementation","text":"","category":"section"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"In our message passing framework one of the most important concepts is the message (wow!). Messages flow along edges of a factor graph and hold information about the part of the graph that it originates from. Usually this information is in a form of a probability distribution. Two common messages are belief propagation messages and variational messages, with are computed differently as shown below.","category":"page"},{"location":"lib/message/#Abstract-message-type","page":"Messages","title":"Abstract message type","text":"","category":"section"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"Both belief propagation and variational messages are subtypes of a AbstractMessage supertype.","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"AbstractMessage","category":"page"},{"location":"lib/message/#ReactiveMP.AbstractMessage","page":"Messages","title":"ReactiveMP.AbstractMessage","text":"AbstractMessage\n\nAn abstract supertype for all concrete message types.\n\nSee also: Message\n\n\n\n\n\n","category":"type"},{"location":"lib/message/#lib-belief-propagation-message","page":"Messages","title":"Belief-Propagation (or Sum-Product) message","text":"","category":"section"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"(Image: message) Belief propagation message","category":"page"},{"location":"lib/message/#lib-variational-message","page":"Messages","title":"Variational message","text":"","category":"section"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"(Image: message) Variational message with structured factorisation q(x, y)q(z) assumption","category":"page"},{"location":"lib/message/#Message-type","page":"Messages","title":"Message type","text":"","category":"section"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"All messages are encoded with the type Message. ","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"Message\nReactiveMP.materialize!","category":"page"},{"location":"lib/message/#ReactiveMP.Message","page":"Messages","title":"ReactiveMP.Message","text":"Message{D, A} <: AbstractMessage\n\nMessage structure encodes a Belief Propagation message, which holds some data that usually a probability distribution, but can also be an arbitrary object. Message acts as a proxy structure to data object and proxies most of the statistical functions, e.g. mean, mode, cov etc.\n\nArguments\n\ndata::D: message always holds some data object associated with it\nis_clamped::Bool, specifies if this message is clamped\nis_initial::Bool, specifies if this message is initial\naddons::A, specifies the addons of the message\n\nExample\n\njulia> distribution = Gamma(10.0, 2.0)\nDistributions.Gamma{Float64}(α=10.0, θ=2.0)\n\njulia> message = Message(distribution, false, true, nothing)\nMessage(Distributions.Gamma{Float64}(α=10.0, θ=2.0))\n\njulia> mean(message) \n20.0\n\njulia> getdata(message)\nDistributions.Gamma{Float64}(α=10.0, θ=2.0)\n\njulia> is_clamped(message)\nfalse\n\njulia> is_initial(message)\ntrue\n\n\nSee also: AbstractMessage, ReactiveMP.materialize!\n\n\n\n\n\n","category":"type"},{"location":"lib/message/#ReactiveMP.materialize!","page":"Messages","title":"ReactiveMP.materialize!","text":"materialize!(message::AbstractMessage)\n\nMaterializes an abstract message and converts it to be of type Message.\n\nSee also: Message\n\n\n\n\n\n","category":"function"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"From an implementation point a view the Message structure does nothing but hold some data object and redirects most of the statistical related functions to that data object. However, this object is used extensively in Julia's multiple dispatch. Our implementation also uses extra is_initial and is_clamped fields to determine if product of two messages results in is_initial or is_clamped posterior marginal. The final field contains the addons. These contain additional information on top of the functional form of the distribution, such as its scaling or computation history.","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"using ReactiveMP, BayesBase, Distributions, ExponentialFamily","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"distribution = NormalMeanPrecision(0.0, 1.0)\nmessage = Message(distribution, false, true, nothing)","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"mean(message), precision(message)","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"logpdf(message, 1.0)","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"is_clamped(message), is_initial(message)","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"The user should not really interact with Message structure while working with ReactiveMP unless doing some advanced inference procedures that involve prediction.","category":"page"},{"location":"lib/nodes/nodes/#lib-node","page":"Overview","title":"Nodes implementation","text":"","category":"section"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"In the message passing framework, one of the most important concepts is a factor node. A factor node represents a local function in a factorised representation of a generative model.","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"note: Note\nTo quickly check the list of all available factor nodes that can be used in the model specification language, call ?make_node or Base.doc(make_node).","category":"page"},{"location":"lib/nodes/nodes/#lib-custom-node","page":"Overview","title":"Adding a custom node","text":"","category":"section"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"ReactiveMP.jl exports the @node macro that allows for quick definition of a factor node with a fixed number of edges. The interface is the following:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"struct MyNewCustomNode end\n\n@node MyNewCustomNode Stochastic [ x, y, z ]\n# ^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^ ^^^^^^^^^^^\n# Node's tag/name Node's type A fixed set of edges\n# Another possible The very first edge (in this example `x`) is considered\n# value is to be the output of the node\n# `Deterministic`","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"This expression registers a new node that can be used with the inference engine. Note howeve, that the @node macro does not generate any message passing update rules. These must be defined using the @rule macro.","category":"page"},{"location":"lib/nodes/nodes/#lib-node-types","page":"Overview","title":"Node types","text":"","category":"section"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"We distinguish different types of factor nodes in order to have better control over Bethe Free Energy computation. Each factor node has either the Deterministic or Stochastic functional form type.","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"Deterministic\nStochastic\nisdeterministic\nisstochastic\nsdtype","category":"page"},{"location":"lib/nodes/nodes/#ReactiveMP.Deterministic","page":"Overview","title":"ReactiveMP.Deterministic","text":"Deterministic\n\nDeterministic object used to parametrize factor node object with determinstic type of relationship between variables.\n\nSee also: Stochastic, isdeterministic, isstochastic, sdtype\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.Stochastic","page":"Overview","title":"ReactiveMP.Stochastic","text":"Stochastic\n\nStochastic object used to parametrize factor node object with stochastic type of relationship between variables.\n\nSee also: Deterministic, isdeterministic, isstochastic, sdtype\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.isdeterministic","page":"Overview","title":"ReactiveMP.isdeterministic","text":"isdeterministic(node)\n\nFunction used to check if factor node object is deterministic or not. Returns true or false.\n\nSee also: Deterministic, Stochastic, isstochastic, sdtype\n\n\n\n\n\n","category":"function"},{"location":"lib/nodes/nodes/#ReactiveMP.isstochastic","page":"Overview","title":"ReactiveMP.isstochastic","text":"isstochastic(node)\n\nFunction used to check if factor node object is stochastic or not. Returns true or false.\n\nSee also: Deterministic, Stochastic, isdeterministic, sdtype\n\n\n\n\n\n","category":"function"},{"location":"lib/nodes/nodes/#ReactiveMP.sdtype","page":"Overview","title":"ReactiveMP.sdtype","text":"sdtype(object)\n\nReturns either Deterministic or Stochastic for a given object (if defined).\n\nSee also: Deterministic, Stochastic, isdeterministic, isstochastic\n\n\n\n\n\n","category":"function"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"using ReactiveMP, BayesBase, Distributions, ExponentialFamily","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"For example the + node has the Deterministic type:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"plus_node = make_node(+)\n\nprintln(\"Is `+` node deterministic: \", isdeterministic(plus_node))\nprintln(\"Is `+` node stochastic: \", isstochastic(plus_node))\nnothing #hide","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"On the other hand, the Bernoulli node has the Stochastic type:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"bernoulli_node = make_node(Bernoulli)\n\nprintln(\"Is `Bernoulli` node deterministic: \", isdeterministic(bernoulli_node))\nprintln(\"Is `Bernoulli` node stochastic: \", isstochastic(bernoulli_node))","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"To get an actual instance of the type object we use sdtype function:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"println(\"sdtype() of `+` node is \", sdtype(plus_node))\nprintln(\"sdtype() of `Bernoulli` node is \", sdtype(bernoulli_node))\nnothing #hide","category":"page"},{"location":"lib/nodes/nodes/#lib-node-functional-dependencies-pipeline","page":"Overview","title":"Node functional dependencies pipeline","text":"","category":"section"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"The generic implementation of factor nodes in ReactiveMP supports custom functional dependency pipelines. Briefly, the functional dependencies pipeline defines what dependencies are need to compute a single message. As an example, consider the belief-propagation message update equation for a factor node f with three edges: x, y and z:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"mu(x) = int mu(y) mu(z) f(x y z) mathrmdy mathrmdz","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"Here we see that in the standard setting for the belief-propagation message out of edge x, we need only messages from the edges y and z. In contrast, consider the variational message update rule equation with mean-field assumption:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"mu(x) = exp int q(y) q(z) log f(x y z) mathrmdy mathrmdz","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"We see that in this setting, we do not need messages mu(y) and mu(z), but only the marginals q(y) and q(z). The purpose of a functional dependencies pipeline is to determine functional dependencies (a set of messages or marginals) that are needed to compute a single message. By default, ReactiveMP.jl uses so-called DefaultFunctionalDependencies that correctly implements belief-propagation and variational message passing schemes (including both mean-field and structured factorisations). The full list of built-in pipelines is presented below:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"ReactiveMP.DefaultFunctionalDependencies\nReactiveMP.RequireMessageFunctionalDependencies\nReactiveMP.RequireMarginalFunctionalDependencies\nReactiveMP.RequireEverythingFunctionalDependencies","category":"page"},{"location":"lib/nodes/nodes/#ReactiveMP.DefaultFunctionalDependencies","page":"Overview","title":"ReactiveMP.DefaultFunctionalDependencies","text":"DefaultFunctionalDependencies\n\nThis pipeline translates directly to enforcing a variational message passing scheme. In order to compute a message out of some edge, this pipeline requires messages from edges within the same edge-cluster and marginals over other edge-clusters.\n\nSee also: ReactiveMP.RequireMessageFunctionalDependencies, ReactiveMP.RequireMarginalFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.RequireMessageFunctionalDependencies","page":"Overview","title":"ReactiveMP.RequireMessageFunctionalDependencies","text":"RequireMessageFunctionalDependencies(indices::Tuple, start_with::Tuple)\n\nThe same as DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the inbound message on the this edge.\n\nArguments\n\nindices::Tuple, tuple of integers, which indicates what edges should require inbound messages\nstart_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial inbound messages for edges in indices\n\nNote: start_with uses setmessage! mechanism, hence, it can be visible by other listeners on the same edge. Explicit call to setmessage! overwrites whatever has been passed in start_with.\n\n@model macro accepts a simplified construction of this pipeline:\n\n@model function some_model()\n # ...\n y ~ NormalMeanVariance(x, τ) where {\n pipeline = RequireMessage(x = vague(NormalMeanPrecision), τ)\n # ^^^ ^^^\n # request 'inbound' for 'x' we may do the same for 'τ',\n # and initialise with `vague(...)` but here we skip initialisation\n }\n # ...\nend\n\nDeprecation warning: RequireInboundFunctionalDependencies has been deprecated in favor of RequireMessageFunctionalDependencies.\n\nSee also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMarginalFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.RequireMarginalFunctionalDependencies","page":"Overview","title":"ReactiveMP.RequireMarginalFunctionalDependencies","text":"RequireMarginalFunctionalDependencies(indices::Tuple, start_with::Tuple)\n\nSimilar to DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the posterior marginal on that edge.\n\nArguments\n\nindices::Tuple, tuple of integers, which indicates what edges should require their own marginals\nstart_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial marginal for edges in indices\n\nNote: start_with uses the setmarginal! mechanism, hence it can be visible to other listeners on the same edge. Explicit calls to setmarginal! overwrites whatever has been passed in start_with.\n\n@model macro accepts a simplified construction of this pipeline:\n\n@model function some_model()\n # ...\n y ~ NormalMeanVariance(x, τ) where {\n pipeline = RequireMarginal(x = vague(NormalMeanPrecision), τ)\n # ^^^ ^^^\n # request 'marginal' for 'x' we may do the same for 'τ',\n # and initialise with `vague(...)` but here we skip initialisation\n }\n # ...\nend\n\nNote: The simplified construction in @model macro syntax is only available in GraphPPL.jl of version >2.2.0.\n\nSee also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMessageFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.RequireEverythingFunctionalDependencies","page":"Overview","title":"ReactiveMP.RequireEverythingFunctionalDependencies","text":"RequireEverythingFunctionalDependencies\n\nThis pipeline specifies that in order to compute a message of some edge update rules request everything that is available locally. This includes all inbound messages (including on the same edge) and marginals over all local edge-clusters (this may or may not include marginals on single edges, depends on the local factorisation constraint).\n\nSee also: DefaultFunctionalDependencies, RequireMessageFunctionalDependencies, RequireMarginalFunctionalDependencies\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#lib-node-traits","page":"Overview","title":"Node traits","text":"","category":"section"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"Each factor node has to define the ReactiveMP.as_node_functional_form trait function and to specify a ReactiveMP.ValidNodeFunctionalForm singleton as a return object. By default ReactiveMP.as_node_functional_form returns ReactiveMP.UndefinedNodeFunctionalForm. Objects that do not specify this property correctly cannot be used in model specification.","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"note: Note\n@node macro does that automatically","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"ReactiveMP.ValidNodeFunctionalForm\nReactiveMP.UndefinedNodeFunctionalForm\nReactiveMP.as_node_functional_form","category":"page"},{"location":"lib/nodes/nodes/#ReactiveMP.ValidNodeFunctionalForm","page":"Overview","title":"ReactiveMP.ValidNodeFunctionalForm","text":"ValidNodeFunctionalForm\n\nTrait specification for an object that can be used in model specification as a factor node.\n\nSee also: ReactiveMP.as_node_functional_form, ReactiveMP.UndefinedNodeFunctionalForm\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.UndefinedNodeFunctionalForm","page":"Overview","title":"ReactiveMP.UndefinedNodeFunctionalForm","text":"UndefinedNodeFunctionalForm\n\nTrait specification for an object that can not be used in model specification as a factor node.\n\nSee also: ReactiveMP.as_node_functional_form, ReactiveMP.ValidNodeFunctionalForm\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.as_node_functional_form","page":"Overview","title":"ReactiveMP.as_node_functional_form","text":"as_node_functional_form(object)\n\nDetermines object node functional form trait specification. Returns either ValidNodeFunctionalForm() or UndefinedNodeFunctionalForm().\n\nSee also: ReactiveMP.ValidNodeFunctionalForm, ReactiveMP.UndefinedNodeFunctionalForm\n\n\n\n\n\n","category":"function"},{"location":"lib/nodes/flow/#lib-nodes-flow","page":"Flow","title":"Flow node","text":"","category":"section"},{"location":"lib/nodes/flow/","page":"Flow","title":"Flow","text":"See also Flow tutorial for a comprehensive guide on using flows in ReactiveMP.","category":"page"},{"location":"lib/nodes/flow/","page":"Flow","title":"Flow","text":"PlanarFlow\nRadialFlow\nFlowModel\nCompiledFlowModel\ncompile\nAdditiveCouplingLayer\nInputLayer\nPermutationLayer\nFlowMeta","category":"page"},{"location":"lib/nodes/flow/#ReactiveMP.PlanarFlow","page":"Flow","title":"ReactiveMP.PlanarFlow","text":"The PlanarFlow function is defined as\n\nf(bfx) = bfx + bfu tanh(bfw^top bfx + b)\n\nwith input and output dimension D. Here bfxin mathbbR^D represents the input of the function. Furthermore bfuin mathbbR^D, bfwin mathbbR^D and binmathbbR represent the parameters of the function. The function contracts and expands the input space. \n\nThis function has been introduced in:\n\nRezende, Danilo, and Shakir Mohamed. \"Variational inference with normalizing flows.\" International conference on machine learning. PMLR, 2015.\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.RadialFlow","page":"Flow","title":"ReactiveMP.RadialFlow","text":"The RadialFlow function is defined as\n\nf(bfx) = bfx + fracbeta(bfz - bfz_0)alpha + bfz - bfz_0\n\nwith input and output dimension D. Here bfxin mathbbR^D represents the input of the function. Furthermore bfz_0in mathbbR^D, alphain mathbbR and betainmathbbR represent the parameters of the function. The function contracts and expands the input space. \n\nThis function has been introduced in:\n\nRezende, Danilo, and Shakir Mohamed. \"Variational inference with normalizing flows.\" International conference on machine learning. PMLR, 2015.\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.FlowModel","page":"Flow","title":"ReactiveMP.FlowModel","text":"The FlowModel structure is the most generic type of Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimensionality and a tuple of layers and can be constructed as FlowModel( dim, (layer1, layer2, ...) ).\n\nNote: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.CompiledFlowModel","page":"Flow","title":"ReactiveMP.CompiledFlowModel","text":"The CompiledFlowModel structure is the most generic type of compiled Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimension and a tuple of compiled layers. Do not manually create a CompiledFlowModel! Instead create a FlowModel first and compile it with compile(model::FlowModel). This will make sure that all layers/mappings are configured with the proper dimensionality and with randomly sampled parameters. Alternatively, if you would like to pass your own parameters, call compile(model::FlowModel, params::Vector).\n\nNote: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.compile","page":"Flow","title":"ReactiveMP.compile","text":"compile() compiles a model by setting its parameters. It randomly sets parameter values in the layers and flows such that inference in the model can be obtained.\n\nInput arguments\n\nmodel::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.\n\nReturn arguments\n\n::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.\n\n\n\n\n\ncompile(model::FlowModel, params::Vector) lets you initialize a model model with a vector of parameters params.\n\nInput arguments\n\nmodel::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.\nparams::Vector - a vector of parameters with which the model should be compiled.\n\nReturn arguments\n\n::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.\n\n\n\n\n\n","category":"function"},{"location":"lib/nodes/flow/#ReactiveMP.AdditiveCouplingLayer","page":"Flow","title":"ReactiveMP.AdditiveCouplingLayer","text":"The additive coupling layer specifies an invertible function bfy = g(bfx) following the specific structure (for the mapping g mathbbR^2 rightarrow mathbbR^2):\n\n beginalign\n y_1 = x_1 \n y_2 = x_2 + f(x_1)\n endalign\n\nwhere f(cdot) denotes an arbitrary function with mapping f mathbbR rightarrow mathbbR. This function can be chosen arbitrarily complex. Non-linear functions (neural networks) are often chosen to model complex relationships. From the definition of the model, invertibility can be easily achieved as\n\n beginalign\n x_1 = y_1 \n x_2 = y_2 - f(y_1)\n endalign\n\nThe current implementation only allows for the mapping g mathbbR^2 rightarrow mathbbR^2, although this layer can be generalized for arbitrary input dimensions.\n\nAdditiveCouplingLayer(f <: AbstractCouplingFlow) creates the layer structure with function f.\n\nExample\n\nf = PlanarFlow()\nlayer = AdditiveCouplingLayer(f)\n\nThis layer structure has been introduced in:\n\nDinh, Laurent, David Krueger, and Yoshua Bengio. \"Nice: Non-linear independent components estimation.\" arXiv preprint arXiv:1410.8516 (2014).\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.InputLayer","page":"Flow","title":"ReactiveMP.InputLayer","text":"The input layer specifies the input dimension to a flow model.\n\nlayer = InputLayer(3)\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.PermutationLayer","page":"Flow","title":"ReactiveMP.PermutationLayer","text":"The permutation layer specifies an invertible mapping bfy = g(bfx) = Pbfx where P is a permutation matrix.\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.FlowMeta","page":"Flow","title":"ReactiveMP.FlowMeta","text":"The FlowMeta structure contains the meta data of the Flow factor node. More specifically, it contains the model of the Flow factor node. The FlowMeta structure can be constructed as FlowMeta(model). Make sure that the flow model has been compiled.\n\nThe FlowMeta structure is required for the Flow factor node and can be included with the Flow node as: y ~ Flow(x) where { meta = FlowMeta(...) }\n\n\n\n\n\n","category":"type"},{"location":"extra/contributing/#Contribution-guidelines","page":"Contributing","title":"Contribution guidelines","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We welcome all possible contributors. This page details the some of the guidelines that should be followed when contributing to this package.","category":"page"},{"location":"extra/contributing/#Reporting-bugs","page":"Contributing","title":"Reporting bugs","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We track bugs using GitHub issues. We encourage you to write complete, specific, reproducible bug reports. Mention the versions of Julia and ReactiveMP for which you observe unexpected behavior. Please provide a concise description of the problem and complement it with code snippets, test cases, screenshots, tracebacks or any other information that you consider relevant. This will help us to replicate the problem and narrow the search space for solutions.","category":"page"},{"location":"extra/contributing/#Suggesting-features","page":"Contributing","title":"Suggesting features","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We welcome new feature proposals. However, before submitting a feature request, consider a few things:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"Does the feature require changes in the core ReactiveMP.jl code? If it doesn't (for example, you would like to add a factor node for a particular application), you can add local extensions in your script/notebook or consider making a separate repository for your extensions.\nIf you would like to add an implementation of a feature that changes a lot in the core ReactiveMP.jl code, please open an issue on GitHub and describe your proposal first. This will allow us to discuss your proposal with you before you invest your time in implementing something that may be difficult to merge later on.","category":"page"},{"location":"extra/contributing/#Contributing-code","page":"Contributing","title":"Contributing code","text":"","category":"section"},{"location":"extra/contributing/#Installing-ReactiveMP","page":"Contributing","title":"Installing ReactiveMP","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We suggest that you use the dev command from the new Julia package manager to install ReactiveMP.jl for development purposes. To work on your fork of ReactiveMP.jl, use your fork's URL address in the dev command, for example:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"] dev git@github.com:your_username/ReactiveMP.jl.git","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"The dev command clones ReactiveMP.jl to ~/.julia/dev/ReactiveMP. All local changes to ReactiveMP code will be reflected in imported code.","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"note: Note\nIt is also might be useful to install Revise.jl package as it allows you to modify code and use the changes without restarting Julia.","category":"page"},{"location":"extra/contributing/#Committing-code","page":"Contributing","title":"Committing code","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We use the standard GitHub Flow workflow where all contributions are added through pull requests. In order to contribute, first fork the repository, then commit your contributions to your fork, and then create a pull request on the master branch of the ReactiveMP.jl repository.","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"Before opening a pull request, please make sure that all tests pass without failing! All demos (can be found in /demo/ directory) and benchmarks (can be found in /benchmark/ directory) have to run without errors as well.","category":"page"},{"location":"extra/contributing/#Style-conventions","page":"Contributing","title":"Style conventions","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"note: Note\nReactiveMP.jl repository contains scripts to automatically format code according to our guidelines. Use make format command to fix code style. This command overwrites files.","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We use default Julia style guide. We list here a few important points and our modifications to the Julia style guide:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"Use 4 spaces for indentation\nType names use UpperCamelCase. For example: AbstractFactorNode, RandomVariable, etc..\nFunction names are lowercase with underscores, when necessary. For example: activate!, randomvar, as_variable, etc..\nVariable names and function arguments use snake_case\nThe name of a method that modifies its argument(s) must end in !","category":"page"},{"location":"extra/contributing/#Unit-tests","page":"Contributing","title":"Unit tests","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We use the test-driven development (TDD) methodology for ReactiveMP.jl development. The test coverage should be as complete as possible. Please make sure that you write tests for each piece of code that you want to add.","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"All unit tests are located in the /test/ directory. The /test/ directory follows the structure of the /src/ directory. Each test file should have following filename format: test_*.jl. Some tests are also present in jldoctest docs annotations directly in the source code. See Julia's documentation about doctests.","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"The tests can be evaluated by running following command in the Julia REPL:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"] test ReactiveMP","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"In addition tests can be evaluated by running following command in the ReactiveMP root directory:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"make test","category":"page"},{"location":"extra/contributing/#Fixes-to-external-libraries","page":"Contributing","title":"Fixes to external libraries","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"If a bug has been discovered in an external dependencies of the ReactiveMP.jl it is the best to open an issue directly in the dependency's github repository. You use can use the fixes.jl file for hot-fixes before a new release of the broken dependecy is available.","category":"page"},{"location":"extra/contributing/#Makefile","page":"Contributing","title":"Makefile","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"ReactiveMP.jl uses Makefile for most common operations:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"make help: Shows help snippet\nmake test: Run tests, supports extra arguments\nmake test test_args=\"distributions:normal_mean_variance\" would run tests only from distributions/test_normal_mean_variance.jl\nmake test test_args=\"distributions:normal_mean_variance models:lgssm\" would run tests both from distributions/test_normal_mean_variance.jl and models/test_lgssm.jl\nmake docs: Compile documentation\nmake benchmark: Run simple benchmark\nmake lint: Check codestyle\nmake format: Check and fix codestyle ","category":"page"},{"location":"extra/extensions/#Extensions-and-interaction-with-the-Julia-ecosystem","page":"Extensions and interaction with the Julia ecosystem","title":"Extensions and interaction with the Julia ecosystem","text":"","category":"section"},{"location":"extra/extensions/","page":"Extensions and interaction with the Julia ecosystem","title":"Extensions and interaction with the Julia ecosystem","text":"ReactiveMP.jl exports extra functionality if other Julia packages are loaded in the same environment.","category":"page"},{"location":"extra/extensions/#Optimisers.jl","page":"Extensions and interaction with the Julia ecosystem","title":"Optimisers.jl","text":"","category":"section"},{"location":"extra/extensions/","page":"Extensions and interaction with the Julia ecosystem","title":"Extensions and interaction with the Julia ecosystem","text":"The Optimizers.jl package defines many standard gradient-based optimisation rules, and tools for applying them to deeply nested models. The optimizers defined in the Optimziers.jl are compatible with the CVI approximation method.","category":"page"},{"location":"extra/extensions/#DiffResults.jl-(loaded-automatically-with-the-ForwardDiff.jl)","page":"Extensions and interaction with the Julia ecosystem","title":"DiffResults.jl (loaded automatically with the ForwardDiff.jl)","text":"","category":"section"},{"location":"extra/extensions/","page":"Extensions and interaction with the Julia ecosystem","title":"Extensions and interaction with the Julia ecosystem","text":"The DiffResults.jl provides the DiffResult type, which can be passed to in-place differentiation methods instead of an output buffer. If loaded in the current Julia session enables faster derivatives with the ForwardDiffGrad option in the CVI approximation method (in the Gaussian case).","category":"page"},{"location":"custom/custom-functional-form/#custom-functional-form","page":"Custom functional form","title":"Custom Functional Form Specification","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"In a nutshell, functional form constraints defines a function that approximates the product of colliding messages and computes posterior marginal that can be used later on during the inference procedure. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. For example, if we refer to our CustomFunctionalForm as to f we can see the whole functional form constraints pipeline as:","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"q(x) = fleft(fracoverrightarrowmu(x)overleftarrowmu(x)int overrightarrowmu(x)overleftarrowmu(x) mathrmdxright)","category":"page"},{"location":"custom/custom-functional-form/#Interface","page":"Custom functional form","title":"Interface","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"ReactiveMP.jl, however, uses some extra utility functions to define functional form constraint behaviour. Here we briefly describe all utility function. If you are only interested in the concrete example, you may directly head to the Custom Functional Form example at the end of this section.","category":"page"},{"location":"custom/custom-functional-form/#Abstract-super-type","page":"Custom functional form","title":"Abstract super type","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"AbstractFormConstraint\nCompositeFormConstraint","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.AbstractFormConstraint","page":"Custom functional form","title":"ReactiveMP.AbstractFormConstraint","text":"AbstractFormConstraint\n\nEvery functional form constraint is a subtype of AbstractFormConstraint abstract type.\n\nNote: this is not strictly necessary, but it makes automatic dispatch easier and compatible with the CompositeFormConstraint.\n\nSee also: CompositeFormConstraint\n\n\n\n\n\n","category":"type"},{"location":"custom/custom-functional-form/#ReactiveMP.CompositeFormConstraint","page":"Custom functional form","title":"ReactiveMP.CompositeFormConstraint","text":"CompositeFormConstraint\n\nCreates a composite form constraint that applies form constraints in order. The composed form constraints must be compatible and have the exact same form_check_strategy. Any functional form constraint that defines is_point_mass_form_constraint() = true may be used only as the last element of the composition.\n\n\n\n\n\n","category":"type"},{"location":"custom/custom-functional-form/#Form-check-strategy","page":"Custom functional form","title":"Form check strategy","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"Every custom functional form must implement a new method for the default_form_check_strategy function that returns either FormConstraintCheckEach or FormConstraintCheckLast.","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"FormConstraintCheckLast: q(x) = f(μ1(x) * μ2(x) * μ3(x))\nFormConstraintCheckEach: q(x) = f(f(μ1(x) * μ2(x)) * μ3(x))","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"default_form_check_strategy\nFormConstraintCheckEach\nFormConstraintCheckLast","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.default_form_check_strategy","page":"Custom functional form","title":"ReactiveMP.default_form_check_strategy","text":"default_form_check_strategy(form_constraint)\n\nReturns a default check strategy (e.g. FormConstraintCheckEach or FormConstraintCheckEach) for a given form constraint object.\n\nSee also: FormConstraintCheckEach, FormConstraintCheckLast, constrain_form\n\n\n\n\n\n","category":"function"},{"location":"custom/custom-functional-form/#ReactiveMP.FormConstraintCheckEach","page":"Custom functional form","title":"ReactiveMP.FormConstraintCheckEach","text":"FormConstraintCheckEach\n\nThis form constraint check strategy checks functional form of the messages product after each product in an equality chain. Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed after each subsequent prod function.\n\nSee also: FormConstraintCheckLast, default_form_check_strategy, constrain_form\n\n\n\n\n\n","category":"type"},{"location":"custom/custom-functional-form/#ReactiveMP.FormConstraintCheckLast","page":"Custom functional form","title":"ReactiveMP.FormConstraintCheckLast","text":"FormConstraintCheckEach\n\nThis form constraint check strategy checks functional form of the last messages product in the equality chain. Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed only once after all subsequenct prod functions have been executed.\n\nSee also: FormConstraintCheckLast, default_form_check_strategy, constrain_form\n\n\n\n\n\n","category":"type"},{"location":"custom/custom-functional-form/#Prod-constraint","page":"Custom functional form","title":"Prod constraint","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"Every custom functional form must implement a new method for the default_prod_constraint function that returns a proper prod_constraint object.","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"default_prod_constraint","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.default_prod_constraint","page":"Custom functional form","title":"ReactiveMP.default_prod_constraint","text":"default_prod_constraint(form_constraint)\n\nReturns a default prod constraint needed to apply a given form_constraint. For most form constraints this function returns ProdGeneric.\n\n\n\n\n\n","category":"function"},{"location":"custom/custom-functional-form/#Constrain-form,-a.k.a-f","page":"Custom functional form","title":"Constrain form, a.k.a f","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"The main function that a custom functional form must implement, which we referred to as f in the beginning of this section, is the constrain_form function.","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"constrain_form","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.constrain_form","page":"Custom functional form","title":"ReactiveMP.constrain_form","text":"constrain_form(form_constraint, distribution)\n\nThis function must approximate distribution object in a form that satisfies form_constraint.\n\nSee also: FormConstraintCheckEach, FormConstraintCheckLast, default_form_check_strategy, is_point_mass_form_constraint\n\n\n\n\n\n","category":"function"},{"location":"custom/custom-functional-form/#Is-point-mass-form-constraint-(optional)","page":"Custom functional form","title":"Is point mass form constraint (optional)","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"Every custom functional form may implement a new method for the is_point_mass_form_constraint function that returns either true or false. This is an utility function that simplifes computation of the Bethe Free Energy and is not strictly necessary.","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"is_point_mass_form_constraint","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.is_point_mass_form_constraint","page":"Custom functional form","title":"ReactiveMP.is_point_mass_form_constraint","text":"is_point_mass_form_constraint(form_constraint)\n\nSpecifies whether form constraint always returns PointMass estimates or not. For a given form_constraint returns either true or false.\n\nSee also: FormConstraintCheckEach, FormConstraintCheckLast, constrain_form\n\n\n\n\n\n","category":"function"},{"location":"custom/custom-functional-form/#Compatibility-with-@constraints-macro-(optional)","page":"Custom functional form","title":"Compatibility with @constraints macro (optional)","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"To make custom functional form constraint compatible with the @constraints macro, it must implement a new method for the make_form_constraint function.","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"make_form_constraint","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.make_form_constraint","page":"Custom functional form","title":"ReactiveMP.make_form_constraint","text":"make_form_constraint(::Type, args...; kwargs...)\n\nCreates form constraint object based on passed type with given args and kwargs. Used to simplify form constraint specification.\n\nAs an example:\n\nmake_form_constraint(PointMass)\n\ncreates an instance of PointMassFormConstraint and \n\nmake_form_constraint(SampleList, 5000, LeftProposal())\n\nshould create an instance of SampleListFormConstraint.\n\nSee also: AbstractFormConstraint\n\n\n\n\n\n","category":"function"},{"location":"custom/custom-functional-form/#custom-functional-form-example","page":"Custom functional form","title":"Custom Functional Form Example","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"In this demo we show how to build a custom functional form constraint that is compatible with the ReactiveMP.jl inference backend. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. We show a relatively simple use-case, which might not be very useful in practice, but serves as a simple step-by-step guide. Assume that we want a specific posterior marginal of some random variable in our model to have a specific Gaussian parametrisation, for example mean-precision. We can use built-in NormalMeanPrecision distribution, but we still need to define our custom functional form constraint:","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"using ReactiveMP, BayesBase\n\n# First we define our functional form structure with no fields\nstruct MeanPrecisionFormConstraint <: AbstractFormConstraint end","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"Next we define the behaviour of our functional form constraint:","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"ReactiveMP.is_point_mass_form_constraint(::MeanPrecisionFormConstraint) = false\nReactiveMP.default_form_check_strategy(::MeanPrecisionFormConstraint) = FormConstraintCheckLast()\nReactiveMP.default_prod_constraint(::MeanPrecisionFormConstraint) = GenericProd()\n\nfunction ReactiveMP.constrain_form(::MeanPrecisionFormConstraint, distribution) \n # This is quite a naive assumption, that a given `dsitribution` object has `mean` and `precision` defined\n # However this quantities might be approximated with some other external method, e.g. Laplace approximation\n m = mean(distribution) # or approximate with some other method\n p = precision(distribution) # or approximate with some other method\n return NormalMeanPrecision(m, p)\nend\n\nfunction ReactiveMP.constrain_form(::MeanPrecisionFormConstraint, distribution::BayesBase.ProductOf)\n # ProductOf is the special case, read about this type more in the corresponding documentation section\n # of the `BayesBase` package\n # ... \nend","category":"page"},{"location":"custom/custom-addons/#custom-addons","page":"Custom addons","title":"Custom Addons","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"Standard message passing schemes only pass along distributions to other nodes. However, for more advanced usage, there might be a need for passing along additional information in messages and/or marginals. One can for example think of passing along the scaling of the distribution or some information that specifies how the message or marginal was computed, i.e. which messages were used for its computation and which node was preceding it. Another use cases is saving extra debugging information inside messages themselves, e.g. what arguments have been used to compute a message.","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"Addons provide a solution here. Basically, addons are structures that contain extra information that are passed along the graph with messages and marginals in a tuple. These addons can be extracted using the getaddons(message/marginal) function. Its usage and operations can differ significantly for each application, yet below gives a concise overview on how to implement them on your own.","category":"page"},{"location":"custom/custom-addons/#Example","page":"Custom addons","title":"Example","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"Suppose that we wish to create an addon that counts the number of computations that preceded some message or marginal. This addon can be created by adding the file src/addons/count.jl and by including it in the ReactiveMP.jl file.","category":"page"},{"location":"custom/custom-addons/#Step-1:-Creating-the-addon-structure","page":"Custom addons","title":"Step 1: Creating the addon structure","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"Let's start by defining our new addon structure. This might seem daunting, but basically only requires us to specify the information that we would like to collect. Just make sure that it is specified as a subtype of AbstractAddon. In our example this becomes:","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"struct AddonCount{T} <: AbstractAddon\n count :: T\nend","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"You can add additional fields or functions for improved handling, such as get_count() or show() functions.","category":"page"},{"location":"custom/custom-addons/#Step-2:-Compute-addon-value-after-computing-a-message","page":"Custom addons","title":"Step 2: Compute addon value after computing a message","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"As a second step we need to specify how the addon behaves when a new message is computed in a factor node. For this purpose we need to implement a specialized version of the message_mapping_addon() function. This function accepts the mapping variables of the factor node and updates the addons by extending the tuple.","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"In our example we could write","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"# This specification assumes that the default value for addon is `AddonCount(nothing)`\nfunction message_mapping_addon(::AddonCount{Nothing}, mapping, messages, marginals, result, addons)\n\n # get number of operations of messages\n message_count = 0\n for message in messages\n message_count += getcount(message)\n end\n\n # get number of operations of marginals\n marginal_count = 0\n for marginal in marginals\n marginal_count += getcount(marginal)\n end\n\n # extend addons with AddonCount() structure\n return AddonCount(message_count + marginal_count + 1)\nend\n","category":"page"},{"location":"custom/custom-addons/#Step-3:-Computing-products","page":"Custom addons","title":"Step 3: Computing products","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"The goal is to update the AddonCount structure when we multiply 2 messages. As a result, we need to write a function that allows us to define this behaviour. This function is called multiply_addons and accepts 5 arguments. In our example this becomes","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"function multiply_addons(left_addon::AddonCount, right_addon::AddonCount, new_dist, left_dist, right_dist)\n return AddonCount(left_addon.count + right_addon.count + 1)\nend","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"here we add the number of operations from the addons that are being multiplied and we add one (for the current operation). we are aware that this is likely not valid for iterative message passing schemes, but it still serves as a nice example. the left_addon and right_addon argument specify the addoncount objects that are being multiplied. corresponding to these addons, there are the distributions left_dist and right_dist, which might contain information for computing the product. the new distribution new_dist ∝ left_dist * right_dist is also passed along for potentially reusing the result of earlier computations.","category":"page"},{"location":"custom/custom-addons/#More-information","page":"Custom addons","title":"More information","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"For more advanced information check the implementation of the log-scale or memory addons.","category":"page"},{"location":"lib/methods/#lib-using-methods","page":"Exported methods","title":"Using methods from ReactiveMP","text":"","category":"section"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"In the Julia programming language (in contrast to Python for example) the most common way of loading a module is:","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"using ReactiveMP","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"A nice explanation about how modules/packages work in Julia can be found in the official documentation.","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"In a nutshell, Julia automatically resolves all name collisions and there is no a lot of benefit of importing specific names, e.g.:","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"import ReactiveMP: mean","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"One of the reasons for that is that Julia uses multiple-dispatch capabilities to merge names automatically and will indicate (with a warning) if something went wrong or names have unresolvable collisions on types. As a small example of this feature consider the following small import example:","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"import ReactiveMP: mean as mean_from_reactivemp\nimport Distributions: mean as mean_from_distributions\nimport BayesBase: mean as mean_from_bayes_base","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"mean_from_reactivemp === mean_from_distributions","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"mean_from_reactivemp === mean_from_bayes_base","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"Even though we import mean function from different packages they actually refer to the same object. Worth noting that this is not always the case - Julia will print a warning in case it finds unresolvable conflicts and usage of such functions will be disallowed unless user import them specifically. Read more about this in the section of the Julia's documentation.","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"# It is easier to let Julia resolve names automatically\n# Julia will not overwrite `mean` that is coming from both packages\nusing ReactiveMP, Distributions, BayesBase, ExponentialFamily","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"mean(Normal(0.0, 1.0)) # `Normal` is an object from `Distributions.jl`","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"mean(NormalMeanVariance(0.0, 1.0)) # `NormalMeanVariance` is an object from `ReactiveMP.jl`","category":"page"},{"location":"lib/methods/#lib-list-methods","page":"Exported methods","title":"List of available methods","text":"","category":"section"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"Below you can find a list of exported methods from ReactiveMP.jl. All methods (even private) can be always accessed with ReactiveMP. prefix, e.g ReactiveMP.mean.","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"note: Note\nSome exported names are (for legacy reasons) intended for private usage only. As a result some of these methods do not have a proper associated documentation with them. We constantly improve ReactiveMP.jl library and continue to add better documentation for many exported methods, but a small portion of these methods could be removed from this list in the future.","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"using ReactiveMP #hide\nforeach(println, names(ReactiveMP))","category":"page"},{"location":"#ReactiveMP.jl","page":"Introduction","title":"ReactiveMP.jl","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"Julia package for reactive message passing Bayesian inference engine on a factor graph.","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"note: Note\nThis package exports only an inference engine, for the full ecosystem with convenient model and constraints specification we refer user to the RxInfer.jl package and its documentation.","category":"page"},{"location":"#Examples-and-tutorials","page":"Introduction","title":"Examples and tutorials","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"Tutorials and examples are available in the RxInfer documentation.","category":"page"},{"location":"#Table-of-Contents","page":"Introduction","title":"Table of Contents","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"Pages = [\n \"lib/message.md\",\n \"lib/node.md\",\n \"lib/math.md\",\n \"extra/contributing.md\"\n]\nDepth = 2","category":"page"},{"location":"#Index","page":"Introduction","title":"Index","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"","category":"page"},{"location":"lib/algebra/common/#lib-helpers-algebra-common","page":"Algebra utils","title":"Algebra common utilities","text":"","category":"section"},{"location":"lib/algebra/common/#lib-helpers-algebra-diageye","page":"Algebra utils","title":"diageye","text":"","category":"section"},{"location":"lib/algebra/common/","page":"Algebra utils","title":"Algebra utils","text":"diageye","category":"page"},{"location":"lib/algebra/common/#ReactiveMP.diageye","page":"Algebra utils","title":"ReactiveMP.diageye","text":"diageye(::Type{T}, n::Int)\n\nAn alias for the Matrix{T}(I, n, n). Returns a matrix of size n x n with ones (of type T) on the diagonal and zeros everywhere else.\n\n\n\n\n\ndiageye(n::Int)\n\nAn alias for the Matrix{Float64}(I, n, n). Returns a matrix of size n x n with ones (of type Float64) on the diagonal and zeros everywhere else.\n\n\n\n\n\n","category":"function"}] +} diff --git a/v3.13.1/siteinfo.js b/v3.13.1/siteinfo.js new file mode 100644 index 000000000..9bfffb7e3 --- /dev/null +++ b/v3.13.1/siteinfo.js @@ -0,0 +1 @@ +var DOCUMENTER_CURRENT_VERSION = "v3.13.1"; diff --git a/v3.13.2/.documenter-siteinfo.json b/v3.13.2/.documenter-siteinfo.json new file mode 100644 index 000000000..91e14ca9c --- /dev/null +++ b/v3.13.2/.documenter-siteinfo.json @@ -0,0 +1 @@ +{"documenter":{"julia_version":"1.10.0","generation_timestamp":"2024-01-25T11:28:31","documenter_version":"1.2.1"}} \ No newline at end of file diff --git a/v3.13.2/assets/documenter.js b/v3.13.2/assets/documenter.js new file mode 100644 index 000000000..f5311607b --- /dev/null +++ b/v3.13.2/assets/documenter.js @@ -0,0 +1,889 @@ +// Generated by Documenter.jl +requirejs.config({ + paths: { + 'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia.min', + 'headroom': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/headroom.min', + 'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.13.2/jquery-ui.min', + 'minisearch': 'https://cdn.jsdelivr.net/npm/minisearch@6.1.0/dist/umd/index.min', + 'katex-auto-render': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/contrib/auto-render.min', + 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.0/jquery.min', + 'headroom-jquery': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/jQuery.headroom.min', + 'katex': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/katex.min', + 'highlight': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min', + 'highlight-julia-repl': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia-repl.min', + }, + shim: { + "highlight-julia": { + "deps": [ + "highlight" + ] + }, + "katex-auto-render": { + "deps": [ + "katex" + ] + }, + "headroom-jquery": { + "deps": [ + "jquery", + "headroom" + ] + }, + "highlight-julia-repl": { + "deps": [ + "highlight" + ] + } +} +}); +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'katex', 'katex-auto-render'], function($, katex, renderMathInElement) { +$(document).ready(function() { + renderMathInElement( + document.body, + { + "delimiters": [ + { + "left": "$", + "right": "$", + "display": false + }, + { + "left": "$$", + "right": "$$", + "display": true + }, + { + "left": "\\[", + "right": "\\]", + "display": true + } + ] +} + + ); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'highlight', 'highlight-julia', 'highlight-julia-repl'], function($) { +$(document).ready(function() { + hljs.highlightAll(); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +let timer = 0; +var isExpanded = true; + +$(document).on("click", ".docstring header", function () { + let articleToggleTitle = "Expand docstring"; + + debounce(() => { + if ($(this).siblings("section").is(":visible")) { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + } else { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + articleToggleTitle = "Collapse docstring"; + } + + $(this) + .find(".docstring-article-toggle-button") + .prop("title", articleToggleTitle); + $(this).siblings("section").slideToggle(); + }); +}); + +$(document).on("click", ".docs-article-toggle-button", function () { + let articleToggleTitle = "Expand docstring"; + let navArticleToggleTitle = "Expand all docstrings"; + + debounce(() => { + if (isExpanded) { + $(this).removeClass("fa-chevron-up").addClass("fa-chevron-down"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + + isExpanded = false; + + $(".docstring section").slideUp(); + } else { + $(this).removeClass("fa-chevron-down").addClass("fa-chevron-up"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + isExpanded = true; + articleToggleTitle = "Collapse docstring"; + navArticleToggleTitle = "Collapse all docstrings"; + + $(".docstring section").slideDown(); + } + + $(this).prop("title", navArticleToggleTitle); + $(".docstring-article-toggle-button").prop("title", articleToggleTitle); + }); +}); + +function debounce(callback, timeout = 300) { + if (Date.now() - timer > timeout) { + callback(); + } + + clearTimeout(timer); + + timer = Date.now(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require([], function() { +function addCopyButtonCallbacks() { + for (const el of document.getElementsByTagName("pre")) { + const button = document.createElement("button"); + button.classList.add("copy-button", "fa-solid", "fa-copy"); + button.setAttribute("aria-label", "Copy this code block"); + button.setAttribute("title", "Copy"); + + el.appendChild(button); + + const success = function () { + button.classList.add("success", "fa-check"); + button.classList.remove("fa-copy"); + }; + + const failure = function () { + button.classList.add("error", "fa-xmark"); + button.classList.remove("fa-copy"); + }; + + button.addEventListener("click", function () { + copyToClipboard(el.innerText).then(success, failure); + + setTimeout(function () { + button.classList.add("fa-copy"); + button.classList.remove("success", "fa-check", "fa-xmark"); + }, 5000); + }); + } +} + +function copyToClipboard(text) { + // clipboard API is only available in secure contexts + if (window.navigator && window.navigator.clipboard) { + return window.navigator.clipboard.writeText(text); + } else { + return new Promise(function (resolve, reject) { + try { + const el = document.createElement("textarea"); + el.textContent = text; + el.style.position = "fixed"; + el.style.opacity = 0; + document.body.appendChild(el); + el.select(); + document.execCommand("copy"); + + resolve(); + } catch (err) { + reject(err); + } finally { + document.body.removeChild(el); + } + }); + } +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", addCopyButtonCallbacks); +} else { + addCopyButtonCallbacks(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'headroom', 'headroom-jquery'], function($, Headroom) { + +// Manages the top navigation bar (hides it when the user starts scrolling down on the +// mobile). +window.Headroom = Headroom; // work around buggy module loading? +$(document).ready(function () { + $("#documenter .docs-navbar").headroom({ + tolerance: { up: 10, down: 10 }, + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'minisearch'], function($, minisearch) { + +// In general, most search related things will have "search" as a prefix. +// To get an in-depth about the thought process you can refer: https://hetarth02.hashnode.dev/series/gsoc + +let results = []; +let timer = undefined; + +let data = documenterSearchIndex["docs"].map((x, key) => { + x["id"] = key; // minisearch requires a unique for each object + return x; +}); + +// list below is the lunr 2.1.3 list minus the intersect with names(Base) +// (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with) +// ideally we'd just filter the original list but it's not available as a variable +const stopWords = new Set([ + "a", + "able", + "about", + "across", + "after", + "almost", + "also", + "am", + "among", + "an", + "and", + "are", + "as", + "at", + "be", + "because", + "been", + "but", + "by", + "can", + "cannot", + "could", + "dear", + "did", + "does", + "either", + "ever", + "every", + "from", + "got", + "had", + "has", + "have", + "he", + "her", + "hers", + "him", + "his", + "how", + "however", + "i", + "if", + "into", + "it", + "its", + "just", + "least", + "like", + "likely", + "may", + "me", + "might", + "most", + "must", + "my", + "neither", + "no", + "nor", + "not", + "of", + "off", + "often", + "on", + "or", + "other", + "our", + "own", + "rather", + "said", + "say", + "says", + "she", + "should", + "since", + "so", + "some", + "than", + "that", + "the", + "their", + "them", + "then", + "there", + "these", + "they", + "this", + "tis", + "to", + "too", + "twas", + "us", + "wants", + "was", + "we", + "were", + "what", + "when", + "who", + "whom", + "why", + "will", + "would", + "yet", + "you", + "your", +]); + +let index = new minisearch({ + fields: ["title", "text"], // fields to index for full-text search + storeFields: ["location", "title", "text", "category", "page"], // fields to return with search results + processTerm: (term) => { + let word = stopWords.has(term) ? null : term; + if (word) { + // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names + word = word + .replace(/^[^a-zA-Z0-9@!]+/, "") + .replace(/[^a-zA-Z0-9@!]+$/, ""); + } + + return word ?? null; + }, + // add . as a separator, because otherwise "title": "Documenter.Anchors.add!", would not find anything if searching for "add!", only for the entire qualification + tokenize: (string) => string.split(/[\s\-\.]+/), + // options which will be applied during the search + searchOptions: { + boost: { title: 100 }, + fuzzy: 2, + processTerm: (term) => { + let word = stopWords.has(term) ? null : term; + if (word) { + word = word + .replace(/^[^a-zA-Z0-9@!]+/, "") + .replace(/[^a-zA-Z0-9@!]+$/, ""); + } + + return word ?? null; + }, + tokenize: (string) => string.split(/[\s\-\.]+/), + }, +}); + +index.addAll(data); + +let filters = [...new Set(data.map((x) => x.category))]; +var modal_filters = make_modal_body_filters(filters); +var filter_results = []; + +$(document).on("keyup", ".documenter-search-input", function (event) { + // Adding a debounce to prevent disruptions from super-speed typing! + debounce(() => update_search(filter_results), 300); +}); + +$(document).on("click", ".search-filter", function () { + if ($(this).hasClass("search-filter-selected")) { + $(this).removeClass("search-filter-selected"); + } else { + $(this).addClass("search-filter-selected"); + } + + // Adding a debounce to prevent disruptions from crazy clicking! + debounce(() => get_filters(), 300); +}); + +/** + * A debounce function, takes a function and an optional timeout in milliseconds + * + * @function callback + * @param {number} timeout + */ +function debounce(callback, timeout = 300) { + clearTimeout(timer); + timer = setTimeout(callback, timeout); +} + +/** + * Make/Update the search component + * + * @param {string[]} selected_filters + */ +function update_search(selected_filters = []) { + let initial_search_body = ` +
Type something to get started!
+ `; + + let querystring = $(".documenter-search-input").val(); + + if (querystring.trim()) { + results = index.search(querystring, { + filter: (result) => { + // Filtering results + if (selected_filters.length === 0) { + return result.score >= 1; + } else { + return ( + result.score >= 1 && selected_filters.includes(result.category) + ); + } + }, + }); + + let search_result_container = ``; + let search_divider = `
`; + + if (results.length) { + let links = []; + let count = 0; + let search_results = ""; + + results.forEach(function (result) { + if (result.location) { + // Checking for duplication of results for the same page + if (!links.includes(result.location)) { + search_results += make_search_result(result, querystring); + count++; + } + + links.push(result.location); + } + }); + + let result_count = `
${count} result(s)
`; + + search_result_container = ` +
+ ${modal_filters} + ${search_divider} + ${result_count} +
+ ${search_results} +
+
+ `; + } else { + search_result_container = ` +
+ ${modal_filters} + ${search_divider} +
0 result(s)
+
+
No result found!
+ `; + } + + if ($(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").removeClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(search_result_container); + } else { + filter_results = []; + modal_filters = make_modal_body_filters(filters, filter_results); + + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(initial_search_body); + } +} + +/** + * Make the modal filter html + * + * @param {string[]} filters + * @param {string[]} selected_filters + * @returns string + */ +function make_modal_body_filters(filters, selected_filters = []) { + let str = ``; + + filters.forEach((val) => { + if (selected_filters.includes(val)) { + str += `${val}`; + } else { + str += `${val}`; + } + }); + + let filter_html = ` +
+ Filters: + ${str} +
+ `; + + return filter_html; +} + +/** + * Make the result component given a minisearch result data object and the value of the search input as queryString. + * To view the result object structure, refer: https://lucaong.github.io/minisearch/modules/_minisearch_.html#searchresult + * + * @param {object} result + * @param {string} querystring + * @returns string + */ +function make_search_result(result, querystring) { + let search_divider = `
`; + let display_link = + result.location.slice(Math.max(0), Math.min(50, result.location.length)) + + (result.location.length > 30 ? "..." : ""); // To cut-off the link because it messes with the overflow of the whole div + + if (result.page !== "") { + display_link += ` (${result.page})`; + } + + let textindex = new RegExp(`\\b${querystring}\\b`, "i").exec(result.text); + let text = + textindex !== null + ? result.text.slice( + Math.max(textindex.index - 100, 0), + Math.min( + textindex.index + querystring.length + 100, + result.text.length + ) + ) + : ""; // cut-off text before and after from the match + + let display_result = text.length + ? "..." + + text.replace( + new RegExp(`\\b${querystring}\\b`, "i"), // For first occurrence + '$&' + ) + + "..." + : ""; // highlights the match + + let in_code = false; + if (!["page", "section"].includes(result.category.toLowerCase())) { + in_code = true; + } + + // We encode the full url to escape some special characters which can lead to broken links + let result_div = ` + +
+
${result.title}
+
${result.category}
+
+

+ ${display_result} +

+
+ ${display_link} +
+ + ${search_divider} + `; + + return result_div; +} + +/** + * Get selected filters, remake the filter html and lastly update the search modal + */ +function get_filters() { + let ele = $(".search-filters .search-filter-selected").get(); + filter_results = ele.map((x) => $(x).text().toLowerCase()); + modal_filters = make_modal_body_filters(filters, filter_results); + update_search(filter_results); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Modal settings dialog +$(document).ready(function () { + var settings = $("#documenter-settings"); + $("#documenter-settings-button").click(function () { + settings.toggleClass("is-active"); + }); + // Close the dialog if X is clicked + $("#documenter-settings button.delete").click(function () { + settings.removeClass("is-active"); + }); + // Close dialog if ESC is pressed + $(document).keyup(function (e) { + if (e.keyCode == 27) settings.removeClass("is-active"); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +let search_modal_header = ` +
+
+

+ + + + +

+
+
+ +
+
+`; + +let initial_search_body = ` +
Type something to get started!
+`; + +let search_modal_footer = ` + +`; + +$(document.body).append( + ` + + ` +); + +document.querySelector(".docs-search-query").addEventListener("click", () => { + openModal(); +}); + +document.querySelector(".close-search-modal").addEventListener("click", () => { + closeModal(); +}); + +$(document).on("click", ".search-result-link", function () { + closeModal(); +}); + +document.addEventListener("keydown", (event) => { + if ((event.ctrlKey || event.metaKey) && event.key === "/") { + openModal(); + } else if (event.key === "Escape") { + closeModal(); + } + + return false; +}); + +// Functions to open and close a modal +function openModal() { + let searchModal = document.querySelector("#search-modal"); + + searchModal.classList.add("is-active"); + document.querySelector(".documenter-search-input").focus(); +} + +function closeModal() { + let searchModal = document.querySelector("#search-modal"); + let initial_search_body = ` +
Type something to get started!
+ `; + + searchModal.classList.remove("is-active"); + document.querySelector(".documenter-search-input").blur(); + + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".documenter-search-input").val(""); + $(".search-modal-card-body").html(initial_search_body); +} + +document + .querySelector("#search-modal .modal-background") + .addEventListener("click", () => { + closeModal(); + }); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Manages the showing and hiding of the sidebar. +$(document).ready(function () { + var sidebar = $("#documenter > .docs-sidebar"); + var sidebar_button = $("#documenter-sidebar-button"); + sidebar_button.click(function (ev) { + ev.preventDefault(); + sidebar.toggleClass("visible"); + if (sidebar.hasClass("visible")) { + // Makes sure that the current menu item is visible in the sidebar. + $("#documenter .docs-menu a.is-active").focus(); + } + }); + $("#documenter > .docs-main").bind("click", function (ev) { + if ($(ev.target).is(sidebar_button)) { + return; + } + if (sidebar.hasClass("visible")) { + sidebar.removeClass("visible"); + } + }); +}); + +// Resizes the package name / sitename in the sidebar if it is too wide. +// Inspired by: https://github.com/davatron5000/FitText.js +$(document).ready(function () { + e = $("#documenter .docs-autofit"); + function resize() { + var L = parseInt(e.css("max-width"), 10); + var L0 = e.width(); + if (L0 > L) { + var h0 = parseInt(e.css("font-size"), 10); + e.css("font-size", (L * h0) / L0); + // TODO: make sure it survives resizes? + } + } + // call once and then register events + resize(); + $(window).resize(resize); + $(window).on("orientationchange", resize); +}); + +// Scroll the navigation bar to the currently selected menu item +$(document).ready(function () { + var sidebar = $("#documenter .docs-menu").get(0); + var active = $("#documenter .docs-menu .is-active").get(0); + if (typeof active !== "undefined") { + sidebar.scrollTop = active.offsetTop - sidebar.offsetTop - 15; + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Theme picker setup +$(document).ready(function () { + // onchange callback + $("#documenter-themepicker").change(function themepick_callback(ev) { + var themename = $("#documenter-themepicker option:selected").attr("value"); + if (themename === "auto") { + // set_theme(window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'); + window.localStorage.removeItem("documenter-theme"); + } else { + // set_theme(themename); + window.localStorage.setItem("documenter-theme", themename); + } + // We re-use the global function from themeswap.js to actually do the swapping. + set_theme_from_local_storage(); + }); + + // Make sure that the themepicker displays the correct theme when the theme is retrieved + // from localStorage + if (typeof window.localStorage !== "undefined") { + var theme = window.localStorage.getItem("documenter-theme"); + if (theme !== null) { + $("#documenter-themepicker option").each(function (i, e) { + e.selected = e.value === theme; + }); + } + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// update the version selector with info from the siteinfo.js and ../versions.js files +$(document).ready(function () { + // If the version selector is disabled with DOCUMENTER_VERSION_SELECTOR_DISABLED in the + // siteinfo.js file, we just return immediately and not display the version selector. + if ( + typeof DOCUMENTER_VERSION_SELECTOR_DISABLED === "boolean" && + DOCUMENTER_VERSION_SELECTOR_DISABLED + ) { + return; + } + + var version_selector = $("#documenter .docs-version-selector"); + var version_selector_select = $("#documenter .docs-version-selector select"); + + version_selector_select.change(function (x) { + target_href = version_selector_select + .children("option:selected") + .get(0).value; + window.location.href = target_href; + }); + + // add the current version to the selector based on siteinfo.js, but only if the selector is empty + if ( + typeof DOCUMENTER_CURRENT_VERSION !== "undefined" && + $("#version-selector > option").length == 0 + ) { + var option = $( + "" + ); + version_selector_select.append(option); + } + + if (typeof DOC_VERSIONS !== "undefined") { + var existing_versions = version_selector_select.children("option"); + var existing_versions_texts = existing_versions.map(function (i, x) { + return x.text; + }); + DOC_VERSIONS.forEach(function (each) { + var version_url = documenterBaseURL + "/../" + each + "/"; + var existing_id = $.inArray(each, existing_versions_texts); + // if not already in the version selector, add it as a new option, + // otherwise update the old option with the URL and enable it + if (existing_id == -1) { + var option = $( + "" + ); + version_selector_select.append(option); + } else { + var option = existing_versions[existing_id]; + option.value = version_url; + option.disabled = false; + } + }); + } + + // only show the version selector if the selector has been populated + if (version_selector_select.children("option").length > 0) { + version_selector.toggleClass("visible"); + } +}); + +}) diff --git a/v3.13.2/assets/img/bp-message.svg b/v3.13.2/assets/img/bp-message.svg new file mode 100644 index 000000000..259b70803 --- /dev/null +++ b/v3.13.2/assets/img/bp-message.svg @@ -0,0 +1 @@ + \ No newline at end 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kbd.button.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,html.theme--documenter-dark .content 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kbd.button.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-primary,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink{background-color:#375a7f;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:hover,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:hover,html.theme--documenter-dark .button.is-primary.is-hovered,html.theme--documenter-dark .docstring>section>a.button.is-hovered.docs-sourcelink{background-color:#335476;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-primary:focus,html.theme--documenter-dark .docstring>section>a.button.docs-sourcelink:focus,html.theme--documenter-dark .button.is-primary.is-focused,html.theme--documenter-dark .docstring>section>a.button.is-focused.docs-sourcelink{border-color:transparent;color:#fff}html.theme--documenter-dark 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.button[disabled],fieldset[disabled] html.theme--documenter-dark .button{background-color:#8c9b9d;border-color:#5e6d6f;box-shadow:none;opacity:.5}html.theme--documenter-dark .button.is-fullwidth{display:flex;width:100%}html.theme--documenter-dark .button.is-loading{color:transparent !important;pointer-events:none}html.theme--documenter-dark .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--documenter-dark .button.is-static{background-color:#282f2f;border-color:#5e6d6f;color:#dbdee0;box-shadow:none;pointer-events:none}html.theme--documenter-dark .button.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--documenter-dark .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--documenter-dark .buttons .button{margin-bottom:0.5rem}html.theme--documenter-dark .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--documenter-dark .buttons:last-child{margin-bottom:-0.5rem}html.theme--documenter-dark .buttons:not(:last-child){margin-bottom:1rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--documenter-dark .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--documenter-dark .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--documenter-dark .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--documenter-dark .buttons.has-addons .button:last-child{margin-right:0}html.theme--documenter-dark .buttons.has-addons .button:hover,html.theme--documenter-dark .buttons.has-addons .button.is-hovered{z-index:2}html.theme--documenter-dark .buttons.has-addons .button:focus,html.theme--documenter-dark .buttons.has-addons .button.is-focused,html.theme--documenter-dark .buttons.has-addons .button:active,html.theme--documenter-dark .buttons.has-addons .button.is-active,html.theme--documenter-dark .buttons.has-addons .button.is-selected{z-index:3}html.theme--documenter-dark .buttons.has-addons .button:focus:hover,html.theme--documenter-dark .buttons.has-addons .button.is-focused:hover,html.theme--documenter-dark .buttons.has-addons .button:active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--documenter-dark .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .buttons.is-centered{justify-content:center}html.theme--documenter-dark .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--documenter-dark .buttons.is-right{justify-content:flex-end}html.theme--documenter-dark .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:1rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1.25rem}}html.theme--documenter-dark .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--documenter-dark .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--documenter-dark .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--documenter-dark 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.progress.is-link::-ms-fill{background-color:#1abc9c}html.theme--documenter-dark .progress.is-link:indeterminate{background-image:linear-gradient(to right, #1abc9c 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-info::-webkit-progress-value{background-color:#024c7d}html.theme--documenter-dark .progress.is-info::-moz-progress-bar{background-color:#024c7d}html.theme--documenter-dark .progress.is-info::-ms-fill{background-color:#024c7d}html.theme--documenter-dark .progress.is-info:indeterminate{background-image:linear-gradient(to right, #024c7d 30%, #343c3d 30%)}html.theme--documenter-dark .progress.is-success::-webkit-progress-value{background-color:#008438}html.theme--documenter-dark .progress.is-success::-moz-progress-bar{background-color:#008438}html.theme--documenter-dark .progress.is-success::-ms-fill{background-color:#008438}html.theme--documenter-dark .progress.is-success:indeterminate{background-image:linear-gradient(to right, #008438 30%, #343c3d 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.hljs-string{color:#38a}.hljs-emphasis{font-style:italic}.hljs-strong{font-weight:bold}.gap-4{gap:1rem} diff --git a/v3.13.2/assets/themeswap.js b/v3.13.2/assets/themeswap.js new file mode 100644 index 000000000..9f5eebe6a --- /dev/null +++ b/v3.13.2/assets/themeswap.js @@ -0,0 +1,84 @@ +// Small function to quickly swap out themes. Gets put into the tag.. +function set_theme_from_local_storage() { + // Initialize the theme to null, which means default + var theme = null; + // If the browser supports the localstorage and is not disabled then try to get the + // documenter theme + if (window.localStorage != null) { + // Get the user-picked theme from localStorage. May be `null`, which means the default + // theme. + theme = window.localStorage.getItem("documenter-theme"); + } + // Check if the users preference is for dark color scheme + var darkPreference = + window.matchMedia("(prefers-color-scheme: dark)").matches === true; + // Initialize a few variables for the loop: + // + // - active: will contain the index of the theme that should be active. Note that there + // is no guarantee that localStorage contains sane values. If `active` stays `null` + // we either could not find the theme or it is the default (primary) theme anyway. + // Either way, we then need to stick to the primary theme. + // + // - disabled: style sheets that should be disabled (i.e. all the theme style sheets + // that are not the currently active theme) + var active = null; + var disabled = []; + var primaryLightTheme = null; + var primaryDarkTheme = null; + for (var i = 0; i < document.styleSheets.length; i++) { + var ss = document.styleSheets[i]; + // The tag of each style sheet is expected to have a data-theme-name attribute + // which must contain the name of the theme. The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // To distinguish the default (primary) theme, it needs to have the data-theme-primary + // attribute set. + if (ss.ownerNode.getAttribute("data-theme-primary") !== null) { + primaryLightTheme = themename; + } + // Check if the theme is primary dark theme so that we could store its name in darkTheme + if (ss.ownerNode.getAttribute("data-theme-primary-dark") !== null) { + primaryDarkTheme = themename; + } + // If we find a matching theme (and it's not the default), we'll set active to non-null + if (themename === theme) active = i; + // Store the style sheets of inactive themes so that we could disable them + if (themename !== theme) disabled.push(ss); + } + var activeTheme = null; + if (active !== null) { + // If we did find an active theme, we'll (1) add the theme--$(theme) class to + document.getElementsByTagName("html")[0].className = "theme--" + theme; + activeTheme = theme; + } else { + // If we did _not_ find an active theme, then we need to fall back to the primary theme + // which can either be dark or light, depending on the user's OS preference. + var activeTheme = darkPreference ? primaryDarkTheme : primaryLightTheme; + // In case it somehow happens that the relevant primary theme was not found in the + // preceding loop, we abort without doing anything. + if (activeTheme === null) { + console.error("Unable to determine primary theme."); + return; + } + // When switching to the primary light theme, then we must not have a class name + // for the tag. 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The names in localStorage much match this. + var themename = ss.ownerNode.getAttribute("data-theme-name"); + // attribute not set => non-theme stylesheet => ignore + if (themename === null) continue; + // we'll disable all the stylesheets, except for the active one + ss.disabled = !(themename == activeTheme); + } +} +set_theme_from_local_storage(); diff --git a/v3.13.2/assets/warner.js b/v3.13.2/assets/warner.js new file mode 100644 index 000000000..3f6f5d008 --- /dev/null +++ b/v3.13.2/assets/warner.js @@ -0,0 +1,52 @@ +function maybeAddWarning() { + // DOCUMENTER_NEWEST is defined in versions.js, DOCUMENTER_CURRENT_VERSION and DOCUMENTER_STABLE + // in siteinfo.js. + // If either of these are undefined something went horribly wrong, so we abort. + if ( + window.DOCUMENTER_NEWEST === undefined || + window.DOCUMENTER_CURRENT_VERSION === undefined || + window.DOCUMENTER_STABLE === undefined + ) { + return; + } + + // Current version is not a version number, so we can't tell if it's the newest version. Abort. + if (!/v(\d+\.)*\d+/.test(window.DOCUMENTER_CURRENT_VERSION)) { + return; + } + + // Current version is newest version, so no need to add a warning. + if (window.DOCUMENTER_NEWEST === window.DOCUMENTER_CURRENT_VERSION) { + return; + } + + // Add a noindex meta tag (unless one exists) so that search engines don't index this version of the docs. + if (document.body.querySelector('meta[name="robots"]') === null) { + const meta = document.createElement("meta"); + meta.name = "robots"; + meta.content = "noindex"; + + document.getElementsByTagName("head")[0].appendChild(meta); + } + + const div = document.createElement("div"); + div.classList.add("outdated-warning-overlay"); + const closer = document.createElement("button"); + closer.classList.add("outdated-warning-closer", "delete"); + closer.addEventListener("click", function () { + document.body.removeChild(div); + }); + const href = window.documenterBaseURL + "/../" + window.DOCUMENTER_STABLE; + div.innerHTML = + 'This documentation is not for the latest stable release, but for either the development version or an older release.
Click here to go to the documentation for the latest stable release.'; + div.appendChild(closer); + document.body.appendChild(div); +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", maybeAddWarning); +} else { + maybeAddWarning(); +} diff --git a/v3.13.2/custom/custom-addons/index.html b/v3.13.2/custom/custom-addons/index.html new file mode 100644 index 000000000..fe4dfc3d2 --- /dev/null +++ b/v3.13.2/custom/custom-addons/index.html @@ -0,0 +1,24 @@ + +Custom addons · ReactiveMP.jl
GitHub

Custom Addons

Standard message passing schemes only pass along distributions to other nodes. However, for more advanced usage, there might be a need for passing along additional information in messages and/or marginals. One can for example think of passing along the scaling of the distribution or some information that specifies how the message or marginal was computed, i.e. which messages were used for its computation and which node was preceding it. Another use cases is saving extra debugging information inside messages themselves, e.g. what arguments have been used to compute a message.

Addons provide a solution here. Basically, addons are structures that contain extra information that are passed along the graph with messages and marginals in a tuple. These addons can be extracted using the getaddons(message/marginal) function. Its usage and operations can differ significantly for each application, yet below gives a concise overview on how to implement them on your own.

Example

Suppose that we wish to create an addon that counts the number of computations that preceded some message or marginal. This addon can be created by adding the file src/addons/count.jl and by including it in the ReactiveMP.jl file.

Step 1: Creating the addon structure

Let's start by defining our new addon structure. This might seem daunting, but basically only requires us to specify the information that we would like to collect. Just make sure that it is specified as a subtype of AbstractAddon. In our example this becomes:

struct AddonCount{T} <: AbstractAddon
+    count :: T
+end

You can add additional fields or functions for improved handling, such as get_count() or show() functions.

Step 2: Compute addon value after computing a message

As a second step we need to specify how the addon behaves when a new message is computed in a factor node. For this purpose we need to implement a specialized version of the message_mapping_addon() function. This function accepts the mapping variables of the factor node and updates the addons by extending the tuple.

In our example we could write

# This specification assumes that the default value for addon is `AddonCount(nothing)`
+function message_mapping_addon(::AddonCount{Nothing}, mapping, messages, marginals, result, addons)
+
+    # get number of operations of messages
+    message_count = 0
+    for message in messages
+        message_count += getcount(message)
+    end
+
+    # get number of operations of marginals
+    marginal_count = 0
+    for marginal in marginals
+        marginal_count += getcount(marginal)
+    end
+
+    # extend addons with AddonCount() structure
+    return AddonCount(message_count + marginal_count + 1)
+end
+

Step 3: Computing products

The goal is to update the AddonCount structure when we multiply 2 messages. As a result, we need to write a function that allows us to define this behaviour. This function is called multiply_addons and accepts 5 arguments. In our example this becomes

function multiply_addons(left_addon::AddonCount, right_addon::AddonCount, new_dist, left_dist, right_dist)
+    return AddonCount(left_addon.count + right_addon.count + 1)
+end

here we add the number of operations from the addons that are being multiplied and we add one (for the current operation). we are aware that this is likely not valid for iterative message passing schemes, but it still serves as a nice example. the left_addon and right_addon argument specify the addoncount objects that are being multiplied. corresponding to these addons, there are the distributions left_dist and right_dist, which might contain information for computing the product. the new distribution new_dist ∝ left_dist * right_dist is also passed along for potentially reusing the result of earlier computations.

More information

For more advanced information check the implementation of the log-scale or memory addons.

diff --git a/v3.13.2/custom/custom-functional-form/index.html b/v3.13.2/custom/custom-functional-form/index.html new file mode 100644 index 000000000..23476fff3 --- /dev/null +++ b/v3.13.2/custom/custom-functional-form/index.html @@ -0,0 +1,21 @@ + +Custom functional form · ReactiveMP.jl
GitHub

Custom Functional Form Specification

In a nutshell, functional form constraints defines a function that approximates the product of colliding messages and computes posterior marginal that can be used later on during the inference procedure. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. For example, if we refer to our CustomFunctionalForm as to f we can see the whole functional form constraints pipeline as:

\[q(x) = f\left(\frac{\overrightarrow{\mu}(x)\overleftarrow{\mu}(x)}{\int \overrightarrow{\mu}(x)\overleftarrow{\mu}(x) \mathrm{d}x}\right)\]

Interface

ReactiveMP.jl, however, uses some extra utility functions to define functional form constraint behaviour. Here we briefly describe all utility function. If you are only interested in the concrete example, you may directly head to the Custom Functional Form example at the end of this section.

Abstract super type

ReactiveMP.AbstractFormConstraint — Type
AbstractFormConstraint

Every functional form constraint is a subtype of AbstractFormConstraint abstract type.

Note: this is not strictly necessary, but it makes automatic dispatch easier and compatible with the CompositeFormConstraint.

See also: CompositeFormConstraint

source
ReactiveMP.CompositeFormConstraint — Type
CompositeFormConstraint

Creates a composite form constraint that applies form constraints in order. The composed form constraints must be compatible and have the exact same form_check_strategy. Any functional form constraint that defines is_point_mass_form_constraint() = true may be used only as the last element of the composition.

source

Form check strategy

Every custom functional form must implement a new method for the default_form_check_strategy function that returns either FormConstraintCheckEach or FormConstraintCheckLast.

  • FormConstraintCheckLast: q(x) = f(μ1(x) * μ2(x) * μ3(x))
  • FormConstraintCheckEach: q(x) = f(f(μ1(x) * μ2(x)) * μ3(x))
ReactiveMP.FormConstraintCheckEach — Type
FormConstraintCheckEach

This form constraint check strategy checks functional form of the messages product after each product in an equality chain. Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed after each subsequent prod function.

See also: FormConstraintCheckLast, default_form_check_strategy, constrain_form

source
ReactiveMP.FormConstraintCheckLast — Type
FormConstraintCheckEach

This form constraint check strategy checks functional form of the last messages product in the equality chain. Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed only once after all subsequenct prod functions have been executed.

See also: FormConstraintCheckLast, default_form_check_strategy, constrain_form

source

Prod constraint

Every custom functional form must implement a new method for the default_prod_constraint function that returns a proper prod_constraint object.

ReactiveMP.default_prod_constraint — Function
default_prod_constraint(form_constraint)

Returns a default prod constraint needed to apply a given form_constraint. For most form constraints this function returns ProdGeneric.

source

Constrain form, a.k.a f

The main function that a custom functional form must implement, which we referred to as f in the beginning of this section, is the constrain_form function.

Is point mass form constraint (optional)

Every custom functional form may implement a new method for the is_point_mass_form_constraint function that returns either true or false. This is an utility function that simplifes computation of the Bethe Free Energy and is not strictly necessary.

Compatibility with @constraints macro (optional)

To make custom functional form constraint compatible with the @constraints macro, it must implement a new method for the make_form_constraint function.

ReactiveMP.make_form_constraint — Function
make_form_constraint(::Type, args...; kwargs...)

Creates form constraint object based on passed type with given args and kwargs. Used to simplify form constraint specification.

As an example:

make_form_constraint(PointMass)

creates an instance of PointMassFormConstraint and 

make_form_constraint(SampleList, 5000, LeftProposal())

should create an instance of SampleListFormConstraint.

See also: AbstractFormConstraint

source

Custom Functional Form Example

In this demo we show how to build a custom functional form constraint that is compatible with the ReactiveMP.jl inference backend. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. We show a relatively simple use-case, which might not be very useful in practice, but serves as a simple step-by-step guide. Assume that we want a specific posterior marginal of some random variable in our model to have a specific Gaussian parametrisation, for example mean-precision. We can use built-in NormalMeanPrecision distribution, but we still need to define our custom functional form constraint:

using ReactiveMP, BayesBase
+
+# First we define our functional form structure with no fields
+struct MeanPrecisionFormConstraint <: AbstractFormConstraint end

Next we define the behaviour of our functional form constraint:

ReactiveMP.is_point_mass_form_constraint(::MeanPrecisionFormConstraint) = false
+ReactiveMP.default_form_check_strategy(::MeanPrecisionFormConstraint)   = FormConstraintCheckLast()
+ReactiveMP.default_prod_constraint(::MeanPrecisionFormConstraint)       = GenericProd()
+
+function ReactiveMP.constrain_form(::MeanPrecisionFormConstraint, distribution)
+    # This is quite a naive assumption, that a given `dsitribution` object has `mean` and `precision` defined
+    # However this quantities might be approximated with some other external method, e.g. Laplace approximation
+    m = mean(distribution)      # or approximate with some other method
+    p = precision(distribution) # or approximate with some other method
+    return NormalMeanPrecision(m, p)
+end
+
+function ReactiveMP.constrain_form(::MeanPrecisionFormConstraint, distribution::BayesBase.ProductOf)
+    # ProductOf is the special case, read about this type more in the corresponding documentation section
+    # of the `BayesBase` package
+    # ...
+end
diff --git a/v3.13.2/extra/contributing/index.html b/v3.13.2/extra/contributing/index.html new file mode 100644 index 000000000..65bc1f4d6 --- /dev/null +++ b/v3.13.2/extra/contributing/index.html @@ -0,0 +1,2 @@ + +Contributing · ReactiveMP.jl
GitHub

Contribution guidelines

We welcome all possible contributors. This page details the some of the guidelines that should be followed when contributing to this package.

Reporting bugs

We track bugs using GitHub issues. We encourage you to write complete, specific, reproducible bug reports. Mention the versions of Julia and ReactiveMP for which you observe unexpected behavior. Please provide a concise description of the problem and complement it with code snippets, test cases, screenshots, tracebacks or any other information that you consider relevant. This will help us to replicate the problem and narrow the search space for solutions.

Suggesting features

We welcome new feature proposals. However, before submitting a feature request, consider a few things:

  • Does the feature require changes in the core ReactiveMP.jl code? If it doesn't (for example, you would like to add a factor node for a particular application), you can add local extensions in your script/notebook or consider making a separate repository for your extensions.
  • If you would like to add an implementation of a feature that changes a lot in the core ReactiveMP.jl code, please open an issue on GitHub and describe your proposal first. This will allow us to discuss your proposal with you before you invest your time in implementing something that may be difficult to merge later on.

Contributing code

Installing ReactiveMP

We suggest that you use the dev command from the new Julia package manager to install ReactiveMP.jl for development purposes. To work on your fork of ReactiveMP.jl, use your fork's URL address in the dev command, for example:

] dev git@github.com:your_username/ReactiveMP.jl.git

The dev command clones ReactiveMP.jl to ~/.julia/dev/ReactiveMP. All local changes to ReactiveMP code will be reflected in imported code.

Note

It is also might be useful to install Revise.jl package as it allows you to modify code and use the changes without restarting Julia.

Committing code

We use the standard GitHub Flow workflow where all contributions are added through pull requests. In order to contribute, first fork the repository, then commit your contributions to your fork, and then create a pull request on the master branch of the ReactiveMP.jl repository.

Before opening a pull request, please make sure that all tests pass without failing! All demos (can be found in /demo/ directory) and benchmarks (can be found in /benchmark/ directory) have to run without errors as well.

Style conventions

Note

ReactiveMP.jl repository contains scripts to automatically format code according to our guidelines. Use make format command to fix code style. This command overwrites files.

We use default Julia style guide. We list here a few important points and our modifications to the Julia style guide:

  • Use 4 spaces for indentation
  • Type names use UpperCamelCase. For example: AbstractFactorNode, RandomVariable, etc..
  • Function names are lowercase with underscores, when necessary. For example: activate!, randomvar, as_variable, etc..
  • Variable names and function arguments use snake_case
  • The name of a method that modifies its argument(s) must end in !

Unit tests

We use the test-driven development (TDD) methodology for ReactiveMP.jl development. The test coverage should be as complete as possible. Please make sure that you write tests for each piece of code that you want to add.

All unit tests are located in the /test/ directory. The /test/ directory follows the structure of the /src/ directory. Each test file should have following filename format: test_*.jl. Some tests are also present in jldoctest docs annotations directly in the source code. See Julia's documentation about doctests.

The tests can be evaluated by running following command in the Julia REPL:

] test ReactiveMP

In addition tests can be evaluated by running following command in the ReactiveMP root directory:

make test

Fixes to external libraries

If a bug has been discovered in an external dependencies of the ReactiveMP.jl it is the best to open an issue directly in the dependency's github repository. You use can use the fixes.jl file for hot-fixes before a new release of the broken dependecy is available.

Makefile

ReactiveMP.jl uses Makefile for most common operations:

  • make help: Shows help snippet
  • make test: Run tests, supports extra arguments
    • make test test_args="distributions:normal_mean_variance" would run tests only from distributions/test_normal_mean_variance.jl
    • make test test_args="distributions:normal_mean_variance models:lgssm" would run tests both from distributions/test_normal_mean_variance.jl and models/test_lgssm.jl
  • make docs: Compile documentation
  • make benchmark: Run simple benchmark
  • make lint: Check codestyle
  • make format: Check and fix codestyle
diff --git a/v3.13.2/extra/extensions/index.html b/v3.13.2/extra/extensions/index.html new file mode 100644 index 000000000..c69f522e0 --- /dev/null +++ b/v3.13.2/extra/extensions/index.html @@ -0,0 +1,2 @@ + +Extensions and interaction with the Julia ecosystem · ReactiveMP.jl
GitHub

Extensions and interaction with the Julia ecosystem

ReactiveMP.jl exports extra functionality if other Julia packages are loaded in the same environment.

Optimisers.jl

The Optimizers.jl package defines many standard gradient-based optimisation rules, and tools for applying them to deeply nested models. The optimizers defined in the Optimziers.jl are compatible with the CVI approximation method.

DiffResults.jl (loaded automatically with the ForwardDiff.jl)

The DiffResults.jl provides the DiffResult type, which can be passed to in-place differentiation methods instead of an output buffer. If loaded in the current Julia session enables faster derivatives with the ForwardDiffGrad option in the CVI approximation method (in the Gaussian case).

diff --git a/v3.13.2/index.html b/v3.13.2/index.html new file mode 100644 index 000000000..c63e51a76 --- /dev/null +++ b/v3.13.2/index.html @@ -0,0 +1,2 @@ + +Introduction · ReactiveMP.jl
GitHub

ReactiveMP.jl

Julia package for reactive message passing Bayesian inference engine on a factor graph.

Note

This package exports only an inference engine, for the full ecosystem with convenient model and constraints specification we refer user to the RxInfer.jl package and its documentation.

Examples and tutorials

Tutorials and examples are available in the RxInfer documentation.

Table of Contents

Index

diff --git a/v3.13.2/lib/algebra/common/index.html b/v3.13.2/lib/algebra/common/index.html new file mode 100644 index 000000000..6f30dcb57 --- /dev/null +++ b/v3.13.2/lib/algebra/common/index.html @@ -0,0 +1,2 @@ + +Algebra utils · ReactiveMP.jl
GitHub

Algebra common utilities

diageye

ReactiveMP.diageye — Function
diageye(::Type{T}, n::Int)

An alias for the Matrix{T}(I, n, n). Returns a matrix of size n x n with ones (of type T) on the diagonal and zeros everywhere else.

source
diageye(n::Int)

An alias for the Matrix{Float64}(I, n, n). Returns a matrix of size n x n with ones (of type Float64) on the diagonal and zeros everywhere else.

source
diff --git a/v3.13.2/lib/helpers/index.html b/v3.13.2/lib/helpers/index.html new file mode 100644 index 000000000..c800ba876 --- /dev/null +++ b/v3.13.2/lib/helpers/index.html @@ -0,0 +1,10 @@ + +Helper utils · ReactiveMP.jl
GitHub

Helper utilities

ReactiveMP implements various structures/functions/methods as "helper" structures that might be useful in various contexts.

SkipIndexIterator

ReactiveMP.SkipIndexIterator — Type
SkipIndexIterator

A special type of iterator that simply iterates over internal iterator, but skips index skip.

Arguments

  • iterator: internal iterator
  • skip: index to skip (integer)

See also: skipindex

source
ReactiveMP.skipindex — Function
skipindex(iterator, skip)

Creation operator for SkipIndexIterator.

julia> s = ReactiveMP.skipindex(1:3, 2)
+2-element ReactiveMP.SkipIndexIterator{Int64, UnitRange{Int64}}:
+ 1
+ 3
+
+julia> collect(s)
+2-element Vector{Int64}:
+ 1
+ 3

See also: SkipIndexIterator

source

FunctionalIndex

ReactiveMP.FunctionalIndex — Type
FunctionalIndex

A special type of an index that represents a function that can be used only in pair with a collection. An example of a FunctionalIndex can be firstindex or lastindex, but more complex use cases are possible too, e.g. firstindex + 1. Important part of the implementation is that the resulting structure is isbitstype(...) = true, that allows to store it in parametric type as valtype.

One use case for this structure is to dispatch on and to replace begin or end (or more complex use cases, e.g. begin + 1) markers in constraints specification language.

source
diff --git a/v3.13.2/lib/message/index.html b/v3.13.2/lib/message/index.html new file mode 100644 index 000000000..323bd4b48 --- /dev/null +++ b/v3.13.2/lib/message/index.html @@ -0,0 +1,20 @@ + +Messages · ReactiveMP.jl
GitHub

Messages implementation

In our message passing framework one of the most important concepts is the message (wow!). Messages flow along edges of a factor graph and hold information about the part of the graph that it originates from. Usually this information is in a form of a probability distribution. Two common messages are belief propagation messages and variational messages, with are computed differently as shown below.

Abstract message type

Both belief propagation and variational messages are subtypes of a AbstractMessage supertype.

Belief-Propagation (or Sum-Product) message

message Belief propagation message

Variational message

message Variational message with structured factorisation q(x, y)q(z) assumption

Message type

All messages are encoded with the type Message.

ReactiveMP.Message — Type
Message{D, A} <: AbstractMessage

Message structure encodes a Belief Propagation message, which holds some data that usually a probability distribution, but can also be an arbitrary object. Message acts as a proxy structure to data object and proxies most of the statistical functions, e.g. mean, mode, cov etc.

Arguments

  • data::D: message always holds some data object associated with it
  • is_clamped::Bool, specifies if this message is clamped
  • is_initial::Bool, specifies if this message is initial
  • addons::A, specifies the addons of the message

Example

julia> distribution = Gamma(10.0, 2.0)
+Distributions.Gamma{Float64}(α=10.0, θ=2.0)
+
+julia> message = Message(distribution, false, true, nothing)
+Message(Distributions.Gamma{Float64}(α=10.0, θ=2.0))
+
+julia> mean(message) 
+20.0
+
+julia> getdata(message)
+Distributions.Gamma{Float64}(α=10.0, θ=2.0)
+
+julia> is_clamped(message)
+false
+
+julia> is_initial(message)
+true
+

See also: AbstractMessage, ReactiveMP.materialize!

source

From an implementation point a view the Message structure does nothing but hold some data object and redirects most of the statistical related functions to that data object. However, this object is used extensively in Julia's multiple dispatch. Our implementation also uses extra is_initial and is_clamped fields to determine if product of two messages results in is_initial or is_clamped posterior marginal. The final field contains the addons. These contain additional information on top of the functional form of the distribution, such as its scaling or computation history.

distribution = NormalMeanPrecision(0.0, 1.0)
+message      = Message(distribution, false, true, nothing)
Message(ExponentialFamily.NormalMeanPrecision{Float64}(μ=0.0, w=1.0))
mean(message), precision(message)
(0.0, 1.0)
logpdf(message, 1.0)
-1.4189385332046727
is_clamped(message), is_initial(message)
(false, true)

The user should not really interact with Message structure while working with ReactiveMP unless doing some advanced inference procedures that involve prediction.

diff --git a/v3.13.2/lib/methods/index.html b/v3.13.2/lib/methods/index.html new file mode 100644 index 000000000..62dc3b352 --- /dev/null +++ b/v3.13.2/lib/methods/index.html @@ -0,0 +1,197 @@ + +Exported methods · ReactiveMP.jl
GitHub

Using methods from ReactiveMP

In the Julia programming language (in contrast to Python for example) the most common way of loading a module is:

using ReactiveMP

A nice explanation about how modules/packages work in Julia can be found in the official documentation.

In a nutshell, Julia automatically resolves all name collisions and there is no a lot of benefit of importing specific names, e.g.:

import ReactiveMP: mean

One of the reasons for that is that Julia uses multiple-dispatch capabilities to merge names automatically and will indicate (with a warning) if something went wrong or names have unresolvable collisions on types. As a small example of this feature consider the following small import example:

import ReactiveMP: mean as mean_from_reactivemp
+import Distributions: mean as mean_from_distributions
+import BayesBase: mean as mean_from_bayes_base
mean_from_reactivemp === mean_from_distributions
true
mean_from_reactivemp === mean_from_bayes_base
true

Even though we import mean function from different packages they actually refer to the same object. Worth noting that this is not always the case - Julia will print a warning in case it finds unresolvable conflicts and usage of such functions will be disallowed unless user import them specifically. Read more about this in the section of the Julia's documentation.

# It is easier to let Julia resolve names automatically
+# Julia will not overwrite `mean` that is coming from both packages
+using ReactiveMP, Distributions, BayesBase, ExponentialFamily
mean(Normal(0.0, 1.0)) # `Normal` is an object from `Distributions.jl`
0.0
mean(NormalMeanVariance(0.0, 1.0)) # `NormalMeanVariance` is an object from `ReactiveMP.jl`
0.0

List of available methods

Below you can find a list of exported methods from ReactiveMP.jl. All methods (even private) can be always accessed with ReactiveMP. prefix, e.g ReactiveMP.mean.

Note

Some exported names are (for legacy reasons) intended for private usage only. As a result some of these methods do not have a proper associated documentation with them. We constantly improve ReactiveMP.jl library and continue to add better documentation for many exported methods, but a small portion of these methods could be removed from this list in the future.

foreach(println, names(ReactiveMP))
@average_energy
+@call_marginalrule
+@call_rule
+@logscale
+@marginalrule
+@node
+@rule
+AND
+AR
+ARMeta
+ARsafe
+ARunsafe
+AbstractApproximationMethod
+AbstractFormConstraint
+AbstractMessage
+AbstractPipelineStage
+AbstractVariable
+Adam
+AdditiveCouplingLayer
+AddonDebug
+AddonLogScale
+AddonMemory
+AsyncPipelineStage
+Autoregressive
+AverageEnergy
+BIFM
+BIFMHelper
+BIFMMeta
+CTMeta
+CTransition
+CVI
+CompanionMatrix
+CompanionMatrixTransposed
+CompiledFlowModel
+CompositeFormConstraint
+CompositePipelineStage
+ConstVariable
+ConstraintsSpecification
+ContinuousTransition
+ContinuousTransitionMeta
+CustomProdStrategy
+DataVariable
+DefaultConstraints
+DefaultMeta
+DeltaFn
+DeltaFnNode
+DeltaMeta
+Deterministic
+DifferentialEntropy
+DiscontinuePipelineStage
+EmptyPipelineStage
+FactorBoundFreeEnergy
+FactorNodeCreationOptions
+FactorNodesCollection
+Flow
+FlowMeta
+FlowModel
+FoldLeftProdStrategy
+FoldRightProdStrategy
+FormConstraintCheckEach
+FormConstraintCheckLast
+FormConstraintCheckPickDefault
+ForwardDiffGrad
+FullFactorisation
+GCV
+GCVMetadata
+GammaMixture
+GammaMixtureNode
+GaussHermiteCubature
+GaussLaguerreQuadrature
+GaussianMixture
+GaussianMixtureNode
+HalfNormal
+IMPLY
+ImportanceSamplingApproximation
+IncludeAll
+InputLayer
+InverseWishart
+LaplaceApproximation
+Linearization
+LoggerPipelineStage
+Marginal
+Marginalisation
+MeanField
+Message
+MetaSpecification
+Mixture
+MixtureNode
+MomentMatching
+MvNormalMeanScalePrecision
+NOT
+NormalMixture
+NormalMixtureNode
+OR
+PermutationLayer
+PermutationMatrix
+PlanarFlow
+Poisson
+Probit
+ProbitMeta
+ProdCVI
+RadialFlow
+RandomVariable
+RandomVariableCreationOptions
+ReactiveMP
+SkipClamped
+SkipClampedAndInitial
+SkipInitial
+SoftDot
+StandardBasisVector
+Stochastic
+Transition
+UT
+Uninformative
+Unscented
+UnscentedTransform
+UnspecifiedConstraints
+UnspecifiedFormConstraint
+UnspecifiedMeta
+VariableBoundEntropy
+VariablesCollection
+VariationalMessage
+apply_pipeline_stage
+approximation_name
+approximation_short_name
+as_marginal
+as_message
+as_variable
+compile
+constrain_form
+constvar
+datavar
+default_form_check_strategy
+default_prod_constraint
+degree
+diageye
+dot
+factorisation
+functional_dependencies
+functionalform
+getapproximation
+getconst
+getconstant
+getdata
+getlayers
+getlogscale
+getmarginal
+getmarginals
+getmemory
+getmodel
+getnode_ids
+getnodes
+getprediction
+getpredictions
+getrandom
+getvardict
+ghcubature
+hasconstvar
+hasdatavar
+hasrandomvar
+huge
+interfaces
+is_clamped
+is_initial
+is_marginalisation
+is_moment_matching
+is_point_mass_form_constraint
+isconnected
+isdeterministic
+isstochastic
+laplace
+localmarginalnames
+localmarginals
+make_form_constraint
+make_node
+marginalrule
+metadata
+name
+nr_params
+randomvar
+rule
+score
+sdtype
+setmarginal!
+setmarginals!
+setmessage!
+setmessages!
+skipindex
+softdot
+srcubature
+tiny
+update!
diff --git a/v3.13.2/lib/nodes/flow/index.html b/v3.13.2/lib/nodes/flow/index.html new file mode 100644 index 000000000..43cf1fceb --- /dev/null +++ b/v3.13.2/lib/nodes/flow/index.html @@ -0,0 +1,9 @@ + +Flow · ReactiveMP.jl
GitHub

Flow node

See also Flow tutorial for a comprehensive guide on using flows in ReactiveMP.

ReactiveMP.PlanarFlow — Type

The PlanarFlow function is defined as

\[f({\bf{x}}) = {\bf{x}} + {\bf{u}} \tanh({\bf{w}}^\top {\bf{x}} + b)\]

with input and output dimension $D$. Here ${\bf{x}}\in \mathbb{R}^D$ represents the input of the function. Furthermore ${\bf{u}}\in \mathbb{R}^D$, ${\bf{w}}\in \mathbb{R}^D$ and $b\in\mathbb{R}$ represent the parameters of the function. The function contracts and expands the input space.

This function has been introduced in:

Rezende, Danilo, and Shakir Mohamed. "Variational inference with normalizing flows." International conference on machine learning. PMLR, 2015.

source
ReactiveMP.RadialFlow — Type

The RadialFlow function is defined as

\[f({\bf{x}}) = {\bf{x}} + \frac{\beta({bf{z}} - {\bf{z}}_0)}{\alpha + |{\bf{z}} - {\bf{z}}_0|}\]

with input and output dimension $D$. Here ${\bf{x}}\in \mathbb{R}^D$ represents the input of the function. Furthermore ${\bf{z}}_0\in \mathbb{R}^D$, $\alpha\in \mathbb{R}$ and $\beta\in\mathbb{R}$ represent the parameters of the function. The function contracts and expands the input space.

This function has been introduced in:

Rezende, Danilo, and Shakir Mohamed. "Variational inference with normalizing flows." International conference on machine learning. PMLR, 2015.

source
ReactiveMP.FlowModel — Type

The FlowModel structure is the most generic type of Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimensionality and a tuple of layers and can be constructed as FlowModel( dim, (layer1, layer2, ...) ).

Note: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.

source
ReactiveMP.CompiledFlowModel — Type

The CompiledFlowModel structure is the most generic type of compiled Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimension and a tuple of compiled layers. Do not manually create a CompiledFlowModel! Instead create a FlowModel first and compile it with compile(model::FlowModel). This will make sure that all layers/mappings are configured with the proper dimensionality and with randomly sampled parameters. Alternatively, if you would like to pass your own parameters, call compile(model::FlowModel, params::Vector).

Note: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.

source
ReactiveMP.compile — Function

compile() compiles a model by setting its parameters. It randomly sets parameter values in the layers and flows such that inference in the model can be obtained.

Input arguments

  • model::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.

Return arguments

  • ::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.
source

compile(model::FlowModel, params::Vector) lets you initialize a model model with a vector of parameters params.

Input arguments

  • model::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.
  • params::Vector - a vector of parameters with which the model should be compiled.

Return arguments

  • ::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.
source
ReactiveMP.AdditiveCouplingLayer — Type

The additive coupling layer specifies an invertible function ${\bf{y}} = g({\bf{x}})$ following the specific structure (for the mapping $g: \mathbb{R}^2 \rightarrow \mathbb{R}^2$):

\[ \begin{align} + y_1 &= x_1 \\ + y_2 &= x_2 + f(x_1) + \end{align}\]

where $f(\cdot)$ denotes an arbitrary function with mapping $f: \mathbb{R} \rightarrow \mathbb{R}$. This function can be chosen arbitrarily complex. Non-linear functions (neural networks) are often chosen to model complex relationships. From the definition of the model, invertibility can be easily achieved as

\[ \begin{align} + x_1 &= y_1 \\ + x_2 &= y_2 - f(y_1) + \end{align}\]

The current implementation only allows for the mapping $g: \mathbb{R}^2 \rightarrow \mathbb{R}^2$, although this layer can be generalized for arbitrary input dimensions.

AdditiveCouplingLayer(f <: AbstractCouplingFlow) creates the layer structure with function f.

Example

f = PlanarFlow()
+layer = AdditiveCouplingLayer(f)

This layer structure has been introduced in:

Dinh, Laurent, David Krueger, and Yoshua Bengio. "Nice: Non-linear independent components estimation." arXiv preprint arXiv:1410.8516 (2014).

source
ReactiveMP.PermutationLayer — Type

The permutation layer specifies an invertible mapping ${\bf{y}} = g({\bf{x}}) = P{\bf{x}}$ where $P$ is a permutation matrix.

source
ReactiveMP.FlowMeta — Type

The FlowMeta structure contains the meta data of the Flow factor node. More specifically, it contains the model of the Flow factor node. The FlowMeta structure can be constructed as FlowMeta(model). Make sure that the flow model has been compiled.

The FlowMeta structure is required for the Flow factor node and can be included with the Flow node as: y ~ Flow(x) where { meta = FlowMeta(...) }

source
diff --git a/v3.13.2/lib/nodes/nodes/index.html b/v3.13.2/lib/nodes/nodes/index.html new file mode 100644 index 000000000..f0f81058f --- /dev/null +++ b/v3.13.2/lib/nodes/nodes/index.html @@ -0,0 +1,37 @@ + +Overview · ReactiveMP.jl
GitHub

Nodes implementation

In the message passing framework, one of the most important concepts is a factor node. A factor node represents a local function in a factorised representation of a generative model.

Note

To quickly check the list of all available factor nodes that can be used in the model specification language, call ?make_node or Base.doc(make_node).

Adding a custom node

ReactiveMP.jl exports the @node macro that allows for quick definition of a factor node with a fixed number of edges. The interface is the following:

struct MyNewCustomNode end
+
+@node MyNewCustomNode   Stochastic         [ x, y, z ]
+#     ^^^^^^^^^^^^^^^   ^^^^^^^^^^^^^      ^^^^^^^^^^^
+#     Node's tag/name   Node's type        A fixed set of edges
+#                       Another possible   The very first edge (in this example `x`) is considered
+#                       value is           to be the output of the node
+#                       `Deterministic`

This expression registers a new node that can be used with the inference engine. Note howeve, that the @node macro does not generate any message passing update rules. These must be defined using the @rule macro.

Node types

We distinguish different types of factor nodes in order to have better control over Bethe Free Energy computation. Each factor node has either the Deterministic or Stochastic functional form type.

For example the + node has the Deterministic type:

plus_node = make_node(+)
+
+println("Is `+` node deterministic: ", isdeterministic(plus_node))
+println("Is `+` node stochastic: ", isstochastic(plus_node))
Is `+` node deterministic: true
+Is `+` node stochastic: false

On the other hand, the Bernoulli node has the Stochastic type:

bernoulli_node = make_node(Bernoulli)
+
+println("Is `Bernoulli` node deterministic: ", isdeterministic(bernoulli_node))
+println("Is `Bernoulli` node stochastic: ", isstochastic(bernoulli_node))
Is `Bernoulli` node deterministic: false
+Is `Bernoulli` node stochastic: true

To get an actual instance of the type object we use sdtype function:

println("sdtype() of `+` node is ", sdtype(plus_node))
+println("sdtype() of `Bernoulli` node is ", sdtype(bernoulli_node))
sdtype() of `+` node is Deterministic()
+sdtype() of `Bernoulli` node is Stochastic()

Node functional dependencies pipeline

The generic implementation of factor nodes in ReactiveMP supports custom functional dependency pipelines. Briefly, the functional dependencies pipeline defines what dependencies are need to compute a single message. As an example, consider the belief-propagation message update equation for a factor node $f$ with three edges: $x$, $y$ and $z$:

\[\mu(x) = \int \mu(y) \mu(z) f(x, y, z) \mathrm{d}y \mathrm{d}z\]

Here we see that in the standard setting for the belief-propagation message out of edge $x$, we need only messages from the edges $y$ and $z$. In contrast, consider the variational message update rule equation with mean-field assumption:

\[\mu(x) = \exp \int q(y) q(z) \log f(x, y, z) \mathrm{d}y \mathrm{d}z\]

We see that in this setting, we do not need messages $\mu(y)$ and $\mu(z)$, but only the marginals $q(y)$ and $q(z)$. The purpose of a functional dependencies pipeline is to determine functional dependencies (a set of messages or marginals) that are needed to compute a single message. By default, ReactiveMP.jl uses so-called DefaultFunctionalDependencies that correctly implements belief-propagation and variational message passing schemes (including both mean-field and structured factorisations). The full list of built-in pipelines is presented below:

ReactiveMP.RequireMessageFunctionalDependencies — Type
RequireMessageFunctionalDependencies(indices::Tuple, start_with::Tuple)

The same as DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the inbound message on the this edge.

Arguments

  • indices::Tuple, tuple of integers, which indicates what edges should require inbound messages
  • start_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial inbound messages for edges in indices

Note: start_with uses setmessage! mechanism, hence, it can be visible by other listeners on the same edge. Explicit call to setmessage! overwrites whatever has been passed in start_with.

@model macro accepts a simplified construction of this pipeline:

@model function some_model()
+    # ...
+    y ~ NormalMeanVariance(x, τ) where {
+        pipeline = RequireMessage(x = vague(NormalMeanPrecision),     τ)
+                                  # ^^^                               ^^^
+                                  # request 'inbound' for 'x'         we may do the same for 'τ',
+                                  # and initialise with `vague(...)`  but here we skip initialisation
+    }
+    # ...
+end

Deprecation warning: RequireInboundFunctionalDependencies has been deprecated in favor of RequireMessageFunctionalDependencies.

See also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMarginalFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies

source
ReactiveMP.RequireMarginalFunctionalDependencies — Type
RequireMarginalFunctionalDependencies(indices::Tuple, start_with::Tuple)

Similar to DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the posterior marginal on that edge.

Arguments

  • indices::Tuple, tuple of integers, which indicates what edges should require their own marginals
  • start_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial marginal for edges in indices

Note: start_with uses the setmarginal! mechanism, hence it can be visible to other listeners on the same edge. Explicit calls to setmarginal! overwrites whatever has been passed in start_with.

@model macro accepts a simplified construction of this pipeline:

@model function some_model()
+    # ...
+    y ~ NormalMeanVariance(x, τ) where {
+        pipeline = RequireMarginal(x = vague(NormalMeanPrecision),     τ)
+                                   # ^^^                               ^^^
+                                   # request 'marginal' for 'x'        we may do the same for 'τ',
+                                   # and initialise with `vague(...)`  but here we skip initialisation
+    }
+    # ...
+end

Note: The simplified construction in @model macro syntax is only available in GraphPPL.jl of version >2.2.0.

See also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMessageFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies

source
ReactiveMP.RequireEverythingFunctionalDependencies — Type

RequireEverythingFunctionalDependencies

This pipeline specifies that in order to compute a message of some edge update rules request everything that is available locally. This includes all inbound messages (including on the same edge) and marginals over all local edge-clusters (this may or may not include marginals on single edges, depends on the local factorisation constraint).

See also: DefaultFunctionalDependencies, RequireMessageFunctionalDependencies, RequireMarginalFunctionalDependencies

source

Node traits

Each factor node has to define the ReactiveMP.as_node_functional_form trait function and to specify a ReactiveMP.ValidNodeFunctionalForm singleton as a return object. By default ReactiveMP.as_node_functional_form returns ReactiveMP.UndefinedNodeFunctionalForm. Objects that do not specify this property correctly cannot be used in model specification.

Note

@node macro does that automatically

diff --git a/v3.13.2/lib/rules/rules/index.html b/v3.13.2/lib/rules/rules/index.html new file mode 100644 index 000000000..53d2dc082 --- /dev/null +++ b/v3.13.2/lib/rules/rules/index.html @@ -0,0 +1,38 @@ + +Message update rules · ReactiveMP.jl
GitHub

Rules implementation

Message update rules

ReactiveMP.rule — Function
rule(fform, on, vconstraint, mnames, messages, qnames, marginals, meta, __node)

This function is used to compute an outbound message for a given node

Arguments

  • fform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)
  • on: Outbound interface's tag for which a message has to be computed, e.g. ::Val{:out} or ::Val{:μ}
  • vconstraint: Variable constraints for an outbound interface, e.g. Marginalisation or MomentMatching
  • mnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • messages: Tuple of message of the same length as mnames used to compute an outbound message
  • qnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • marginals: Tuple of marginals of the same length as qnames used to compute an outbound message
  • meta: Extra meta information
  • addons: Extra addons information
  • __node: Node reference

For all available rules, see ReactiveMP.print_rules_table().

See also: @rule, marginalrule, @marginalrule

source
ReactiveMP.@rule — Macro
@rule NodeType(:Edge, Constraint) (Arguments..., [ meta::MetaType ]) = begin
+    # rule body
+    return ...
+end

The @rule macro help to define new methods for the rule function. It works particularly well in combination with the @node macro. It has a specific structure, which must specify:

  • NodeType: must be a valid Julia type. If some attempt to define a rule for a Julia function (for example +), use typeof(+)
  • Edge: edge label, usually edge labels are defined with the @node macro
  • Constrain: DEPRECATED, please just use the Marginalisation label
  • Arguments: defines a list of the input arguments for the rule
    • m_* prefix indicates that the argument is of type Message from the edge *
    • q_* prefix indicates that the argument is of type Marginal on the edge *
  • Meta::MetaType - optionally, a user can specify a Meta object of type MetaType. This can be useful is some attempts to try different rules with different approximation methods or if the rule itself requires some temporary storage or cache. The default meta is nothing.

Here are various examples of the @rule macro usage:

  1. Belief-Propagation (or Sum-Product) message update rule for the NormalMeanVariance node toward the :μ edge with the Marginalisation constraint. Input arguments are m_out and m_v, which are the messages from the corresponding edges out and v and have the type PointMass.
@rule NormalMeanVariance(:μ, Marginalisation) (m_out::PointMass, m_v::PointMass) = NormalMeanVariance(mean(m_out), mean(m_v))
  1. Mean-field message update rule for the NormalMeanVariance node towards the :μ edge with the Marginalisation constraint. Input arguments are q_out and q_v, which are the marginals on the corresponding edges out and v of type Any.
@rule NormalMeanVariance(:μ, Marginalisation) (q_out::Any, q_v::Any) = NormalMeanVariance(mean(q_out), mean(q_v))
  1. Structured Variational message update rule for the NormalMeanVariance node towards the :out edge with the Marginalisation constraint. Input arguments are m_μ, which is a message from the μ edge of type UnivariateNormalDistributionsFamily, and q_v, which is a marginal on the v edge of type Any.
@rule NormalMeanVariance(:out, Marginalisation) (m_μ::UnivariateNormalDistributionsFamily, q_v::Any) = begin
+    m_μ_mean, m_μ_cov = mean_cov(m_μ)
+    return NormalMeanVariance(m_μ_mean, m_μ_cov + mean(q_v))
+end

See also: rule, marginalrule, [@marginalrule], @call_rule

source
ReactiveMP.@call_rule — Macro
@call_rule NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])

The @call_rule macro helps to call the rule method with an easier syntax. The structure of the macro is almost the same as in the @rule macro, but there is no begin ... end block, but instead each argument must have a specified value with the = operator.

The @call_rule accepts optional list of options before the functional form specification, for example:

@call_rule [ return_addons = true ] NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])

The list of available options is:

  • return_addons - forces the @call_rule to return the tuple of (result, addons)

See also: @rule, rule, @call_marginalrule

source
ReactiveMP.print_rules_table — Function
Prints a table of all message passing update rules in ReactiveMP.
+
+Use `Markdown.parse` on the output of this function to get a prettified table.
source

Marginal update rules

ReactiveMP.marginalrule — Function
marginalrule(fform, on, mnames, messages, qnames, marginals, meta, __node)

This function is used to compute a local joint marginal for a given node

Arguments

  • fform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)
  • on: Local joint marginal tag , e.g. ::Val{ :mean_precision } or ::Val{ :out_mean_precision }
  • mnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • messages: Tuple of message of the same length as mnames used to compute an outbound message
  • qnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • marginals: Tuple of marginals of the same length as qnames used to compute an outbound message
  • meta: Extra meta information
  • __node: Node reference

See also: rule, @rule @marginalrule

source
ReactiveMP.@marginalrule — Macro
@marginalrule NodeType(:Cluster) (Arguments..., [ meta::MetaType ]) = begin
+    # rule body
+    return ...
+end

The @marginalrule macro help to define new methods for the marginalrule function. It works particularly well in combination with the @node macro. It has a specific structure, which must specify:

  • NodeType: must be a valid Julia type. If some attempt to define a rule for a Julia function (for example +), use typeof(+)
  • Cluster: edge cluster that contains joined edge labels with the _ symbol. Usually edge labels are defined with the @node macro
  • Arguments: defines a list of the input arguments for the rule
    • m_* prefix indicates that the argument is of type Message from the edge *
    • q_* prefix indicates that the argument is of type Marginal on the edge *
  • Meta::MetaType - optionally, a user can specify a Meta object of type MetaType. This can be useful is some attempts to try different rules with different approximation methods or if the rule itself requires some temporary storage or cache. The default meta is nothing.

The @marginalrule can return a NamedTuple in the return statement. This would indicate some variables in the joint marginal for the Cluster are independent and the joint itself is factorised. For example if some attempts to compute a marginal for the q(x, y) it is possible to return (x = ..., y = ...) as the result of the computation to indicate that q(x, y) = q(x)q(y).

Here are various examples of the @marginalrule macro usage:

  1. Marginal computation rule around the NormalMeanPrecision node for the q(out, μ). The rule accepts arguments m_out and m_μ, which are the messages

from the out and μ edges respectively, and q_τ which is the marginal on the edge τ.

@marginalrule NormalMeanPrecision(:out_μ) (m_out::UnivariateNormalDistributionsFamily, m_μ::UnivariateNormalDistributionsFamily, q_τ::Any) = begin
+    xi_out, W_out = weightedmean_precision(m_out)
+    xi_μ, W_μ     = weightedmean_precision(m_μ)
+
+    W_bar = mean(q_τ)
+
+    W  = [W_out+W_bar -W_bar; -W_bar W_μ+W_bar]
+    xi = [xi_out; xi_μ]
+
+    return MvNormalWeightedMeanPrecision(xi, W)
+end
  1. Marginal computation rule around the NormalMeanPrecision node for the q(out, μ). The rule accepts arguments m_out and m_μ, which are the messages from the

out and μ edges respectively, and q_τ which is the marginal on the edge τ. In this example the result of the computation is a NamedTuple

@marginalrule NormalMeanPrecision(:out_μ) (m_out::PointMass, m_μ::UnivariateNormalDistributionsFamily, q_τ::Any) = begin
+    return (out = m_out, μ = prod(ClosedProd(), NormalMeanPrecision(mean(m_out), mean(q_τ)), m_μ))
+end
source
ReactiveMP.@call_marginalrule — Macro
@call_marginalrule NodeType(:edge) (argument1 = value1, argument2 = value2, ..., [ meta = ... ])

The @call_marginalrule macro helps to call the marginalrule method with an easier syntax. The structure of the macro is almost the same as in the @marginalrule macro, but there is no begin ... end block, but instead each argument must have a specified value with the = operator.

See also: @marginalrule, marginalrule, @call_rule

source

Testing utilities for the update rules

ReactiveMP.@test_rules — Macro
@test_rules [options] rule [ test_entries... ]

The @test_rules macro generates test cases for message update rules for probabilistic programming models that follow the "message passing" paradigm. It takes a rule specification as input and generates a set of tests based on that specification. This macro is provided by ReactiveMP.

Note: The Test module must be imported explicitly. The @test_rules macro tries to use the @test macro, which must be defined globally.

Arguments

The macro takes three arguments:

  • options: An optional argument that specifies the options for the test generation process. See below for details.
  • rule: A rule specification in the same format as the @rule macro, e.g. Beta(:out, Marginalisation) or NormalMeanVariance(:μ, Marginalisation).
  • test_entries: An array of named tuples (input = ..., output = ...). The input entry has the same format as the input for the @rule macro. The output entry specifies the expected output.

Options

The following options are available:

  • check_type_promotion: By default, this option is set to false. If set to true, the macro generates an extensive list of extra tests that aim to check the correct type promotion within the tests. For example, if all inputs are of type Float32, then the expected output should also be of type Float32. See the paramfloattype and convert_paramfloattype functions for details.
  • atol: Sets the desired accuracy for the tests. The tests use the custom_rule_isapprox function from ReactiveMP to check if outputs are approximately the same. This argument can be either a single number or an array of key => value pairs.
  • extra_float_types: A set of extra float types to be used in the check_type_promotion tests. This argument has no effect if check_type_promotion is set to false.

The default values for the atol option are:

  • Float32: 1e-4
  • Float64: 1e-6
  • BigFloat: 1e-8

Examples


+@test_rules [check_type_promotion = true] Beta(:out, Marginalisation) [
+    (input = (m_a = PointMass(1.0), m_b = PointMass(2.0)), output = Beta(1.0, 2.0)),
+    (input = (m_a = PointMass(2.0), m_b = PointMass(2.0)), output = Beta(2.0, 2.0)),
+    (input = (m_a = PointMass(3.0), m_b = PointMass(3.0)), output = Beta(3.0, 3.0))
+]
+
+@test_rules [check_type_promotion = true] Beta(:out, Marginalisation) [
+    (input = (q_a = PointMass(1.0), q_b = PointMass(2.0)), output = Beta(1.0, 2.0)),
+    (input = (q_a = PointMass(2.0), q_b = PointMass(2.0)), output = Beta(2.0, 2.0)),
+    (input = (q_a = PointMass(3.0), q_b = PointMass(3.0)), output = Beta(3.0, 3.0))
+]

See also: ReactiveMP.@test_marginalrules

source

Table of available update rules

using ReactiveMP, Markdown
+Markdown.parse(ReactiveMP.print_rules_table())
NodeOutputInputsMeta
*inμ(A) :: BayesBase.PointMass{<:LinearAlgebra.UniformScaling}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
*inμ(A) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: BayesBase.PointMass{<:Real}
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecision
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: ExponentialFamily.MvNormalMeanPrecision
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: ExponentialFamily.MvNormalMeanCovariance
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*inμ(out) :: Distributions.UnivariateDistributionUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: Distributions.UnivariateDistribution
*inμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*inμ(out) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass
*inμ(out) :: ExponentialFamily.GammaDistributionsFamilyUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:Real}
*inμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractMatrix}
*inμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractVector}
*inμ(out) :: FUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:Real}
*inμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractMatrix}
*inμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractVector}
*outμ(A) :: Distributions.UnivariateDistributionUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Distributions.UnivariateDistribution
*outμ(A) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*outμ(A) :: BayesBase.PointMass{<:LinearAlgebra.UniformScaling}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
*outμ(A) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.MvNormalWeightedMeanPrecision
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.MvNormalMeanPrecision
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.MvNormalMeanCovariance
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*outμ(A) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.GammaDistributionsFamily
*outμ(A) :: ExponentialFamily.GammaDistributionsFamilyUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*outμ(A) :: BayesBase.PointMass{<:AbstractMatrix}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: F
*outμ(A) :: FUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractMatrix}
*outμ(A) :: BayesBase.PointMass{<:AbstractVector}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*outμ(A) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractVector}
*Aμ(out) :: Distributions.UnivariateDistributionUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Distributions.UnivariateDistribution
*Aμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*Aμ(out) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass
*Aμ(out) :: ExponentialFamily.GammaDistributionsFamilyUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*Aμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractMatrix}
*Aμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractVector}
*Aμ(out) :: FUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*Aμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractMatrix}
*Aμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractVector}
+in2μ(out) :: Any
μ(in1) :: Any
-in2μ(out) :: Any
μ(in1) :: Any
-in1μ(out) :: Any
μ(in2) :: Any
-outμ(in1) :: Any
μ(in2) :: Any
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision
+in1μ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T1, M, P} where {M<:AbstractVector{T1}, P<:AbstractMatrix{T1}}Nothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T1, M, P} where {M<:AbstractVector{T1}, P<:AbstractMatrix{T1}}Nothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T2, M, P} where {M<:AbstractVector{T2}, P<:AbstractMatrix{T2}}
+in1μ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+in1μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+in1μ(out) :: ExponentialFamily.MvNormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalMeanPrecision
+in1μ(out) :: ExponentialFamily.NormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.NormalMeanPrecision
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+in1μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision
+outμ(in1) :: ExponentialFamily.MvNormalWeightedMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T1, M, P} where {M<:AbstractVector{T1}, P<:AbstractMatrix{T1}}Nothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T2, M, P} where {M<:AbstractVector{T2}, P<:AbstractMatrix{T2}}
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalMeanPrecision
+outμ(in1) :: ExponentialFamily.MvNormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.NormalMeanPrecision
+outμ(in1) :: ExponentialFamily.NormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Distributions.DistributionNothing
μ(in2) :: Distributions.Distribution
dotin2μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in1) :: BayesBase.PointMass
dotin1μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in2) :: BayesBase.PointMass
dotoutμ(in1) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in2) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
dotoutμ(in1) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in2) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceΣq(out_μ) :: AnyNothing
ExponentialFamily.MvNormalMeanCovarianceΣq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: BayesBase.PointMassNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutq(μ) :: BayesBase.PointMassNothing
q(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceoutq(μ) :: AnyNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: BayesBase.PointMassNothing
μ(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: BayesBase.PointMassNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceμq(out) :: BayesBase.PointMassNothing
q(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceμq(out) :: AnyNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: BayesBase.PointMassNothing
μ(Σ) :: BayesBase.PointMass
Distributions.Dirichletoutq(a) :: BayesBase.PointMass{<:AbstractVector}Nothing
ExponentialFamily.MatrixDirichletoutq(a) :: BayesBase.PointMassNothing
Distributions.Dirichletoutμ(a) :: BayesBase.PointMass{<:AbstractVector}Nothing
ExponentialFamily.MatrixDirichletoutμ(a) :: BayesBase.PointMassNothing
BIFMzprevμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
μ(znext) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
BIFMznextμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
μ(zprev) :: BayesBase.TerminalProdArgument{<:Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T}
BIFMoutμ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(zprev) :: BayesBase.TerminalProdArgument{<:Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T}
μ(znext) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
BIFMinμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(zprev) :: BayesBase.TerminalProdArgument{<:Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T}
μ(znext) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
Flowinμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TFlowMeta{M, <:Unscented}
Flowinμ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecisionFlowMeta{M, <:Linearization}
Flowinμ(out) :: ExponentialFamily.MvNormalMeanPrecisionFlowMeta{M, <:Linearization}
Flowinμ(out) :: ExponentialFamily.MvNormalMeanCovarianceFlowMeta{M, <:Linearization}
Flowoutμ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TFlowMeta{M, <:Unscented}
Flowoutμ(in) :: ExponentialFamily.MvNormalWeightedMeanPrecisionFlowMeta{M, <:Linearization}
Flowoutμ(in) :: ExponentialFamily.MvNormalMeanPrecisionFlowMeta{M, <:Linearization}
Flowoutμ(in) :: ExponentialFamily.MvNormalMeanCovarianceFlowMeta{M, <:Linearization}
Distributions.Categorical{P} where P<:Realpq(out) :: BayesBase.PointMassNothing
Distributions.Bernoullipq(out) :: Distributions.Categorical{P} where P<:RealNothing
Distributions.Categorical{P} where P<:Realpq(out) :: AnyNothing
Distributions.Bernoullipq(out) :: Distributions.BernoulliNothing
Distributions.Bernoullipq(out) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutq(p) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutq(p) :: Distributions.DirichletNothing
Distributions.Bernoullioutq(p) :: AnyNothing
Distributions.Bernoullioutq(p) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutμ(p) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutμ(p) :: Distributions.DirichletNothing
Distributions.Bernoullioutμ(p) :: BayesBase.PointMassNothing
Distributions.Bernoullioutμ(p) :: Distributions.BetaNothing
Mixtureswitchμ(out) :: AnyNothing
μ(inputs) :: ManyOf{<:Tuple{Vararg{Any, N}}}
Mixtureoutμ(inputs) :: ManyOf{<:Tuple{Vararg{Any, N}}}Nothing
q(switch) :: BayesBase.PointMass
Mixtureoutμ(switch) :: AnyNothing
μ(inputs) :: ManyOf{<:Tuple{Vararg{Any, N}}}
Mixtureinputsμ(out) :: AnyNothing
q(switch) :: BayesBase.PointMass
Mixtureinputsμ(out) :: AnyNothing
μ(switch) :: Any
NOToutμ(in) :: Distributions.BernoulliNothing
Probitoutμ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, ProbitMeta}
Probitoutμ(in) :: BayesBase.PointMassUnion{Nothing, ProbitMeta}
NOTinμ(out) :: Distributions.BernoulliNothing
Probitinμ(out) :: Union{BayesBase.PointMass, Distributions.Bernoulli}Union{Nothing, ProbitMeta}
BIFMHelperinμ(out) :: AnyNothing
ExponentialFamily.MvNormalWeightedMeanPrecisionoutq(ξ) :: AnyNothing
q(Λ) :: Any
ExponentialFamily.MvNormalWeightedMeanPrecisionoutμ(ξ) :: BayesBase.PointMassNothing
μ(Λ) :: BayesBase.PointMass
HalfNormaloutq(v) :: BayesBase.PointMassNothing
Probitoutq(in) :: BayesBase.PointMassUnion{Nothing, ProbitMeta}
BIFMHelperoutq(in) :: AnyNothing
Probitinμ(out) :: Union{BayesBase.PointMass, Distributions.Bernoulli}Union{Nothing, ProbitMeta}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
Probitinμ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, ProbitMeta}
q(out) :: BayesBase.PointMass
Probitinq(out) :: BayesBase.PointMassUnion{Nothing, ProbitMeta}
Distributions.Betaoutq(a) :: BayesBase.PointMassNothing
q(b) :: BayesBase.PointMass
Distributions.Uniformoutq(a) :: BayesBase.PointMassNothing
q(b) :: BayesBase.PointMass
Distributions.Betaoutμ(a) :: BayesBase.PointMassNothing
μ(b) :: BayesBase.PointMass
Distributions.Uniformoutμ(a) :: BayesBase.PointMassNothing
μ(b) :: BayesBase.PointMass
MvNormalMeanScalePrecisionγq(out_μ) :: AnyNothing
MvNormalMeanScalePrecisionγq(out) :: AnyNothing
q(μ) :: Any
MvNormalMeanScalePrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(γ) :: Any
MvNormalMeanScalePrecisionμq(out) :: AnyNothing
q(γ) :: Any
MvNormalMeanScalePrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(γ) :: Any
MvNormalMeanScalePrecisionoutq(μ) :: AnyNothing
q(γ) :: Any
Distributions.Uniformoutμ(b) :: BayesBase.PointMassNothing
q(a) :: BayesBase.PointMass
Distributions.Uniformoutμ(a) :: BayesBase.PointMassNothing
q(b) :: BayesBase.PointMass
Distributions.Bernoullipμ(out) :: BayesBase.PointMassNothing
ExponentialFamily.NormalMeanVariancevq(out_μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
ExponentialFamily.NormalMeanVariancevq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.NormalMeanVariancevμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
ExponentialFamily.NormalMeanVariancevμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(μ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVariancevμ(out) :: BayesBase.PointMassNothing
μ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
ExponentialFamily.NormalMeanVarianceμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceμμ(out) :: BayesBase.PointMassNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
q(v) :: Any
ExponentialFamily.NormalMeanVarianceμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμμ(out) :: BayesBase.PointMassNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμq(out) :: BayesBase.PointMassNothing
q(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμq(out) :: AnyNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: BayesBase.PointMassNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutq(μ) :: BayesBase.PointMassNothing
q(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceoutq(μ) :: AnyNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: BayesBase.PointMassNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionΛq(out_μ) :: AnyNothing
ExponentialFamily.MvNormalMeanPrecisionΛq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Distributions.Wishart
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutq(μ) :: BayesBase.PointMassNothing
q(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionoutq(μ) :: AnyNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Distributions.Wishart
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionμq(out) :: BayesBase.PointMassNothing
q(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionμq(out) :: AnyNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
μ(Λ) :: BayesBase.PointMass
InverseWishartoutq(ν) :: AnyNothing
q(S) :: Any
Distributions.Wishartoutq(ν) :: AnyNothing
q(S) :: Any
InverseWishartoutμ(ν) :: BayesBase.PointMassNothing
q(S) :: Any
Distributions.Wishartoutμ(ν) :: BayesBase.PointMassNothing
q(S) :: Any
InverseWishartoutμ(S) :: BayesBase.PointMassNothing
q(ν) :: Any
Distributions.Wishartoutμ(S) :: BayesBase.PointMassNothing
q(ν) :: Any
InverseWishartoutμ(ν) :: BayesBase.PointMassNothing
μ(S) :: BayesBase.PointMass
Distributions.Wishartoutμ(ν) :: BayesBase.PointMassNothing
μ(S) :: BayesBase.PointMass
Poissonoutq(l) :: ExponentialFamily.GammaDistributionsFamilyNothing
Poissonoutμ(l) :: BayesBase.PointMassNothing
Poissonlq(out) :: AnyNothing
Poissonlμ(out) :: BayesBase.PointMassNothing
GammaMixture{N}outq(switch) :: AnyNothing
q(a) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(b) :: ExponentialFamily.GammaDistributionsFamily
GammaMixturebq(out) :: AnyNothing
q(switch) :: Any
q(a) :: Any
GammaMixtureaq(out) :: AnyNothing
q(switch) :: Any
q(b) :: ExponentialFamily.GammaDistributionsFamily
GammaMixture{N}switchq(out) :: AnyNothing
q(a) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(b) :: ExponentialFamily.GammaDistributionsFamily
ContinuousTransitionWq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
ContinuousTransitionaq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
q(W) :: Any
ContinuousTransitionxμ(y) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
q(W) :: Any
ContinuousTransitionyμ(x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
q(W) :: Any
DeltaFnoutq(ins) :: BayesBase.FactorizedJoint{P}DeltaMeta{M}
DeltaFnoutq(ins) :: BayesBase.FactorizedJointDeltaMeta{M}
DeltaFnoutμ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M}
DeltaFnoutμ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M}
DeltaFninμ(in) :: AnyDeltaMeta{M}
q(ins) :: BayesBase.FactorizedJoint
DeltaFninμ(in) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, Nothing}
q(ins) :: ExponentialFamily.JointNormal
DeltaFninμ(in) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
q(ins) :: ExponentialFamily.JointNormal
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Nothing
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Nothing
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
ANDoutμ(in1) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
ORoutμ(in1) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
ANDin2μ(out) :: Distributions.BernoulliNothing
μ(in1) :: Distributions.Bernoulli
ORin2μ(out) :: Distributions.BernoulliNothing
μ(in1) :: Distributions.Bernoulli
ANDin1μ(out) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
ORin1μ(out) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
GCVyq(x) :: AnyUnion{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
GCVyμ(x) :: Union{ReactiveMP.ExponentialLinearQuadratic, ExponentialFamily.UnivariateNormalDistributionsFamily, ExponentialFamily.UnivariateGaussianDistributionsFamily}Union{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
GCVzq(y) :: AnyGCVMetadata
q(x) :: Any
q(κ) :: Any
q(ω) :: Any
GCVzq(y_x) :: AnyGCVMetadata
q(κ) :: Any
q(ω) :: Any
GCVκq(y) :: AnyGCVMetadata
q(x) :: Any
q(z) :: Any
q(ω) :: Any
GCVκq(y_x) :: AnyGCVMetadata
q(z) :: Any
q(ω) :: Any
GCVωq(y) :: AnyGCVMetadata
q(x) :: Any
q(z) :: Any
q(κ) :: Any
GCVωq(y_x) :: AnyGCVMetadata
q(z) :: Any
q(κ) :: Any
GCVxq(y) :: AnyUnion{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
GCVxμ(y) :: Union{ReactiveMP.ExponentialLinearQuadratic, ExponentialFamily.UnivariateNormalDistributionsFamily, ExponentialFamily.UnivariateGaussianDistributionsFamily}Union{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
ExponentialFamily.NormalMeanPrecisionτq(out_μ) :: AnyNothing
ExponentialFamily.NormalMeanPrecisionτq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμq(out) :: BayesBase.PointMassNothing
q(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμq(out) :: AnyNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutq(μ) :: BayesBase.PointMassNothing
q(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionoutq(μ) :: AnyNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
μ(τ) :: BayesBase.PointMass
Distributions.InverseGammaoutq(α) :: AnyNothing
q(θ) :: Any
Distributions.Gammaoutq(α) :: AnyNothing
q(θ) :: Any
Distributions.InverseGammaoutμ(α) :: BayesBase.PointMassNothing
μ(θ) :: BayesBase.PointMass
Distributions.Gammaoutμ(α) :: BayesBase.PointMassNothing
μ(θ) :: BayesBase.PointMass
SoftDotγq(y) :: AnyNothing
q(θ) :: Any
q(x) :: Any
SoftDotθq(y) :: AnyNothing
q(x) :: Any
q(γ) :: Any
SoftDotxq(y) :: AnyNothing
q(θ) :: Any
q(γ) :: Any
SoftDotyq(θ) :: AnyNothing
q(x) :: Any
q(γ) :: Any
Transitioninq(out) :: BayesBase.PointMassNothing
q(a) :: BayesBase.PointMass
Transitioninq(out) :: AnyNothing
q(a) :: ExponentialFamily.MatrixDirichlet
Transitioninμ(out) :: Distributions.Categorical{P} where P<:Real
q(a) :: BayesBase.PointMass
Transitioninμ(out) :: Distributions.Categorical{P} where P<:RealNothing
q(a) :: ExponentialFamily.MatrixDirichlet
Transitioninμ(out) :: Distributions.Categorical{P} where P<:RealNothing
μ(a) :: BayesBase.PointMass
Transitionoutμ(in) :: Distributions.DiscreteNonParametric
q(a) :: BayesBase.PointMass
Transitionoutμ(in) :: Distributions.Categorical{P} where P<:RealNothing
q(a) :: Distributions.Distribution{Distributions.Matrixvariate, Distributions.Continuous}
Transitionoutq(in) :: Distributions.Categorical{P} where P<:RealNothing
q(a) :: Any
Transitionoutq(in) :: BayesBase.PointMassNothing
q(a) :: BayesBase.PointMass
Transitionoutμ(in) :: Distributions.Categorical{P} where P<:RealNothing
μ(a) :: BayesBase.PointMass
Transitionaq(out_in) :: BayesBase.ContingencyNothing
Transitionaq(out) :: AnyNothing
q(in) :: Distributions.Categorical{P} where P<:Real
ARyq(x) :: AnyARMeta
q(θ) :: Any
q(γ) :: Any
ARyμ(x) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TARMeta
q(θ) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
q(γ) :: Any
ARθq(y) :: AnyARMeta
q(x) :: Any
q(γ) :: Any
ARθq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TARMeta
q(γ) :: Any
ARxq(y) :: AnyARMeta
q(θ) :: Any
q(γ) :: Any
ARxμ(y) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TARMeta
q(θ) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
q(γ) :: Any
ARγq(y) :: AnyARMeta
q(x) :: Any
q(θ) :: Any
ARγq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TARMeta
q(θ) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
NormalMixture{N}outq(switch) :: AnyNothing
q(m) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(p) :: ManyOf{<:Tuple{Vararg{Any, N}}}
NormalMixturepq(out) :: AnyNothing
q(switch) :: Any
q(m) :: Any
NormalMixturemq(out) :: AnyNothing
q(switch) :: Any
q(p) :: Any
NormalMixture{N}switchq(out) :: AnyNothing
q(m) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(p) :: ManyOf{<:Tuple{Vararg{Any, N}}}
ExponentialFamily.GammaShapeRateαq(out) :: Distributions.GammaNothing
q(β) :: Distributions.Gamma
ExponentialFamily.GammaShapeRateoutq(α) :: BayesBase.PointMassNothing
q(β) :: BayesBase.PointMass
ExponentialFamily.GammaShapeRateoutμ(α) :: BayesBase.PointMassNothing
μ(β) :: BayesBase.PointMass
outμ(in1) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
in2μ(out) :: Distributions.BernoulliNothing
μ(in1) :: Distributions.Bernoulli
in1μ(out) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
+
diff --git a/v3.13.2/search_index.js b/v3.13.2/search_index.js new file mode 100644 index 000000000..8d55bd0c1 --- /dev/null +++ b/v3.13.2/search_index.js @@ -0,0 +1,3 @@ +var documenterSearchIndex = {"docs": +[{"location":"lib/helpers/#lib-helpers","page":"Helper utils","title":"Helper utilities","text":"","category":"section"},{"location":"lib/helpers/","page":"Helper utils","title":"Helper utils","text":"ReactiveMP implements various structures/functions/methods as \"helper\" structures that might be useful in various contexts.","category":"page"},{"location":"lib/helpers/#lib-helpers-skip-index-iterator","page":"Helper utils","title":"SkipIndexIterator","text":"","category":"section"},{"location":"lib/helpers/","page":"Helper utils","title":"Helper utils","text":"ReactiveMP.SkipIndexIterator\nReactiveMP.skipindex","category":"page"},{"location":"lib/helpers/#ReactiveMP.SkipIndexIterator","page":"Helper utils","title":"ReactiveMP.SkipIndexIterator","text":"SkipIndexIterator\n\nA special type of iterator that simply iterates over internal iterator, but skips index skip.\n\nArguments\n\niterator: internal iterator\nskip: index to skip (integer)\n\nSee also: skipindex\n\n\n\n\n\n","category":"type"},{"location":"lib/helpers/#ReactiveMP.skipindex","page":"Helper utils","title":"ReactiveMP.skipindex","text":"skipindex(iterator, skip)\n\nCreation operator for SkipIndexIterator.\n\njulia> s = ReactiveMP.skipindex(1:3, 2)\n2-element ReactiveMP.SkipIndexIterator{Int64, UnitRange{Int64}}:\n 1\n 3\n\njulia> collect(s)\n2-element Vector{Int64}:\n 1\n 3\n\nSee also: SkipIndexIterator\n\n\n\n\n\n","category":"function"},{"location":"lib/helpers/#lib-helpers-functional-index","page":"Helper utils","title":"FunctionalIndex","text":"","category":"section"},{"location":"lib/helpers/","page":"Helper utils","title":"Helper utils","text":"ReactiveMP.FunctionalIndex","category":"page"},{"location":"lib/helpers/#ReactiveMP.FunctionalIndex","page":"Helper utils","title":"ReactiveMP.FunctionalIndex","text":"FunctionalIndex\n\nA special type of an index that represents a function that can be used only in pair with a collection. An example of a FunctionalIndex can be firstindex or lastindex, but more complex use cases are possible too, e.g. firstindex + 1. Important part of the implementation is that the resulting structure is isbitstype(...) = true, that allows to store it in parametric type as valtype.\n\nOne use case for this structure is to dispatch on and to replace begin or end (or more complex use cases, e.g. begin + 1) markers in constraints specification language.\n\n\n\n\n\n","category":"type"},{"location":"lib/rules/rules/#lib-rules","page":"Message update rules","title":"Rules implementation","text":"","category":"section"},{"location":"lib/rules/rules/#lib-message-rules","page":"Message update rules","title":"Message update rules","text":"","category":"section"},{"location":"lib/rules/rules/","page":"Message update rules","title":"Message update rules","text":"rule\n@rule\n@call_rule\nReactiveMP.print_rules_table","category":"page"},{"location":"lib/rules/rules/#ReactiveMP.rule","page":"Message update rules","title":"ReactiveMP.rule","text":"rule(fform, on, vconstraint, mnames, messages, qnames, marginals, meta, __node)\n\nThis function is used to compute an outbound message for a given node\n\nArguments\n\nfform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)\non: Outbound interface's tag for which a message has to be computed, e.g. ::Val{:out} or ::Val{:μ}\nvconstraint: Variable constraints for an outbound interface, e.g. Marginalisation or MomentMatching\nmnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }\nmessages: Tuple of message of the same length as mnames used to compute an outbound message\nqnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }\nmarginals: Tuple of marginals of the same length as qnames used to compute an outbound message\nmeta: Extra meta information\naddons: Extra addons information\n__node: Node reference\n\nFor all available rules, see ReactiveMP.print_rules_table().\n\nSee also: @rule, marginalrule, @marginalrule\n\n\n\n\n\n","category":"function"},{"location":"lib/rules/rules/#ReactiveMP.@rule","page":"Message update rules","title":"ReactiveMP.@rule","text":"@rule NodeType(:Edge, Constraint) (Arguments..., [ meta::MetaType ]) = begin\n # rule body\n return ...\nend\n\nThe @rule macro help to define new methods for the rule function. It works particularly well in combination with the @node macro. It has a specific structure, which must specify:\n\nNodeType: must be a valid Julia type. If some attempt to define a rule for a Julia function (for example +), use typeof(+)\nEdge: edge label, usually edge labels are defined with the @node macro\nConstrain: DEPRECATED, please just use the Marginalisation label\nArguments: defines a list of the input arguments for the rule\nm_* prefix indicates that the argument is of type Message from the edge *\nq_* prefix indicates that the argument is of type Marginal on the edge *\nMeta::MetaType - optionally, a user can specify a Meta object of type MetaType. This can be useful is some attempts to try different rules with different approximation methods or if the rule itself requires some temporary storage or cache. The default meta is nothing.\n\nHere are various examples of the @rule macro usage:\n\nBelief-Propagation (or Sum-Product) message update rule for the NormalMeanVariance node toward the :μ edge with the Marginalisation constraint. Input arguments are m_out and m_v, which are the messages from the corresponding edges out and v and have the type PointMass.\n\n@rule NormalMeanVariance(:μ, Marginalisation) (m_out::PointMass, m_v::PointMass) = NormalMeanVariance(mean(m_out), mean(m_v))\n\nMean-field message update rule for the NormalMeanVariance node towards the :μ edge with the Marginalisation constraint. Input arguments are q_out and q_v, which are the marginals on the corresponding edges out and v of type Any.\n\n@rule NormalMeanVariance(:μ, Marginalisation) (q_out::Any, q_v::Any) = NormalMeanVariance(mean(q_out), mean(q_v))\n\nStructured Variational message update rule for the NormalMeanVariance node towards the :out edge with the Marginalisation constraint. Input arguments are m_μ, which is a message from the μ edge of type UnivariateNormalDistributionsFamily, and q_v, which is a marginal on the v edge of type Any.\n\n@rule NormalMeanVariance(:out, Marginalisation) (m_μ::UnivariateNormalDistributionsFamily, q_v::Any) = begin\n m_μ_mean, m_μ_cov = mean_cov(m_μ)\n return NormalMeanVariance(m_μ_mean, m_μ_cov + mean(q_v))\nend\n\nSee also: rule, marginalrule, [@marginalrule], @call_rule\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#ReactiveMP.@call_rule","page":"Message update rules","title":"ReactiveMP.@call_rule","text":"@call_rule NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])\n\nThe @call_rule macro helps to call the rule method with an easier syntax. The structure of the macro is almost the same as in the @rule macro, but there is no begin ... end block, but instead each argument must have a specified value with the = operator.\n\nThe @call_rule accepts optional list of options before the functional form specification, for example:\n\n@call_rule [ return_addons = true ] NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])\n\nThe list of available options is:\n\nreturn_addons - forces the @call_rule to return the tuple of (result, addons)\n\nSee also: @rule, rule, @call_marginalrule\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#ReactiveMP.print_rules_table","page":"Message update rules","title":"ReactiveMP.print_rules_table","text":"Prints a table of all message passing update rules in ReactiveMP.\n\nUse `Markdown.parse` on the output of this function to get a prettified table.\n\n\n\n\n\n","category":"function"},{"location":"lib/rules/rules/#lib-marginal-rules","page":"Message update rules","title":"Marginal update rules","text":"","category":"section"},{"location":"lib/rules/rules/","page":"Message update rules","title":"Message update rules","text":"marginalrule\n@marginalrule\n@call_marginalrule","category":"page"},{"location":"lib/rules/rules/#ReactiveMP.marginalrule","page":"Message update rules","title":"ReactiveMP.marginalrule","text":"marginalrule(fform, on, mnames, messages, qnames, marginals, meta, __node)\n\nThis function is used to compute a local joint marginal for a given node\n\nArguments\n\nfform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)\non: Local joint marginal tag , e.g. ::Val{ :mean_precision } or ::Val{ :out_mean_precision }\nmnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }\nmessages: Tuple of message of the same length as mnames used to compute an outbound message\nqnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }\nmarginals: Tuple of marginals of the same length as qnames used to compute an outbound message\nmeta: Extra meta information\n__node: Node reference\n\nSee also: rule, @rule @marginalrule\n\n\n\n\n\n","category":"function"},{"location":"lib/rules/rules/#ReactiveMP.@marginalrule","page":"Message update rules","title":"ReactiveMP.@marginalrule","text":"@marginalrule NodeType(:Cluster) (Arguments..., [ meta::MetaType ]) = begin\n # rule body\n return ...\nend\n\nThe @marginalrule macro help to define new methods for the marginalrule function. It works particularly well in combination with the @node macro. It has a specific structure, which must specify:\n\nNodeType: must be a valid Julia type. If some attempt to define a rule for a Julia function (for example +), use typeof(+)\nCluster: edge cluster that contains joined edge labels with the _ symbol. Usually edge labels are defined with the @node macro\nArguments: defines a list of the input arguments for the rule\nm_* prefix indicates that the argument is of type Message from the edge *\nq_* prefix indicates that the argument is of type Marginal on the edge *\nMeta::MetaType - optionally, a user can specify a Meta object of type MetaType. This can be useful is some attempts to try different rules with different approximation methods or if the rule itself requires some temporary storage or cache. The default meta is nothing.\n\nThe @marginalrule can return a NamedTuple in the return statement. This would indicate some variables in the joint marginal for the Cluster are independent and the joint itself is factorised. For example if some attempts to compute a marginal for the q(x, y) it is possible to return (x = ..., y = ...) as the result of the computation to indicate that q(x, y) = q(x)q(y).\n\nHere are various examples of the @marginalrule macro usage:\n\nMarginal computation rule around the NormalMeanPrecision node for the q(out, μ). The rule accepts arguments m_out and m_μ, which are the messages \n\nfrom the out and μ edges respectively, and q_τ which is the marginal on the edge τ.\n\n@marginalrule NormalMeanPrecision(:out_μ) (m_out::UnivariateNormalDistributionsFamily, m_μ::UnivariateNormalDistributionsFamily, q_τ::Any) = begin\n xi_out, W_out = weightedmean_precision(m_out)\n xi_μ, W_μ = weightedmean_precision(m_μ)\n\n W_bar = mean(q_τ)\n\n W = [W_out+W_bar -W_bar; -W_bar W_μ+W_bar]\n xi = [xi_out; xi_μ]\n\n return MvNormalWeightedMeanPrecision(xi, W)\nend\n\nMarginal computation rule around the NormalMeanPrecision node for the q(out, μ). The rule accepts arguments m_out and m_μ, which are the messages from the \n\nout and μ edges respectively, and q_τ which is the marginal on the edge τ. In this example the result of the computation is a NamedTuple\n\n@marginalrule NormalMeanPrecision(:out_μ) (m_out::PointMass, m_μ::UnivariateNormalDistributionsFamily, q_τ::Any) = begin\n return (out = m_out, μ = prod(ClosedProd(), NormalMeanPrecision(mean(m_out), mean(q_τ)), m_μ))\nend\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#ReactiveMP.@call_marginalrule","page":"Message update rules","title":"ReactiveMP.@call_marginalrule","text":"@call_marginalrule NodeType(:edge) (argument1 = value1, argument2 = value2, ..., [ meta = ... ])\n\nThe @call_marginalrule macro helps to call the marginalrule method with an easier syntax. The structure of the macro is almost the same as in the @marginalrule macro, but there is no begin ... end block, but instead each argument must have a specified value with the = operator.\n\nSee also: @marginalrule, marginalrule, @call_rule\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#lib-rules-tests","page":"Message update rules","title":"Testing utilities for the update rules","text":"","category":"section"},{"location":"lib/rules/rules/","page":"Message update rules","title":"Message update rules","text":"ReactiveMP.@test_rules\nReactiveMP.@test_marginalrules","category":"page"},{"location":"lib/rules/rules/#ReactiveMP.@test_rules","page":"Message update rules","title":"ReactiveMP.@test_rules","text":"@test_rules [options] rule [ test_entries... ]\n\nThe @test_rules macro generates test cases for message update rules for probabilistic programming models that follow the \"message passing\" paradigm. It takes a rule specification as input and generates a set of tests based on that specification. This macro is provided by ReactiveMP.\n\nNote: The Test module must be imported explicitly. The @test_rules macro tries to use the @test macro, which must be defined globally.\n\nArguments\n\nThe macro takes three arguments:\n\noptions: An optional argument that specifies the options for the test generation process. See below for details.\nrule: A rule specification in the same format as the @rule macro, e.g. Beta(:out, Marginalisation) or NormalMeanVariance(:μ, Marginalisation).\ntest_entries: An array of named tuples (input = ..., output = ...). The input entry has the same format as the input for the @rule macro. The output entry specifies the expected output.\n\nOptions\n\nThe following options are available:\n\ncheck_type_promotion: By default, this option is set to false. If set to true, the macro generates an extensive list of extra tests that aim to check the correct type promotion within the tests. For example, if all inputs are of type Float32, then the expected output should also be of type Float32. See the paramfloattype and convert_paramfloattype functions for details.\natol: Sets the desired accuracy for the tests. The tests use the custom_rule_isapprox function from ReactiveMP to check if outputs are approximately the same. This argument can be either a single number or an array of key => value pairs.\nextra_float_types: A set of extra float types to be used in the check_type_promotion tests. This argument has no effect if check_type_promotion is set to false.\n\nThe default values for the atol option are:\n\nFloat32: 1e-4\nFloat64: 1e-6\nBigFloat: 1e-8\n\nExamples\n\n\n@test_rules [check_type_promotion = true] Beta(:out, Marginalisation) [\n (input = (m_a = PointMass(1.0), m_b = PointMass(2.0)), output = Beta(1.0, 2.0)),\n (input = (m_a = PointMass(2.0), m_b = PointMass(2.0)), output = Beta(2.0, 2.0)),\n (input = (m_a = PointMass(3.0), m_b = PointMass(3.0)), output = Beta(3.0, 3.0))\n]\n\n@test_rules [check_type_promotion = true] Beta(:out, Marginalisation) [\n (input = (q_a = PointMass(1.0), q_b = PointMass(2.0)), output = Beta(1.0, 2.0)),\n (input = (q_a = PointMass(2.0), q_b = PointMass(2.0)), output = Beta(2.0, 2.0)),\n (input = (q_a = PointMass(3.0), q_b = PointMass(3.0)), output = Beta(3.0, 3.0))\n]\n\nSee also: ReactiveMP.@test_marginalrules\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#ReactiveMP.@test_marginalrules","page":"Message update rules","title":"ReactiveMP.@test_marginalrules","text":"@test_marginalrules [options] rule [ test_entries... ]\n\nEffectively the same as @test_rules, but for marginal computation rules. See the documentation for @test_rules for more info.\n\nSee also: ReactiveMP.@test_rules\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#lib-rules-table","page":"Message update rules","title":"Table of available update rules","text":"","category":"section"},{"location":"lib/rules/rules/","page":"Message update rules","title":"Message update rules","text":"using ReactiveMP, Markdown\nMarkdown.parse(ReactiveMP.print_rules_table())","category":"page"},{"location":"lib/message/#lib-message","page":"Messages","title":"Messages implementation","text":"","category":"section"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"In our message passing framework one of the most important concepts is the message (wow!). Messages flow along edges of a factor graph and hold information about the part of the graph that it originates from. Usually this information is in a form of a probability distribution. Two common messages are belief propagation messages and variational messages, with are computed differently as shown below.","category":"page"},{"location":"lib/message/#Abstract-message-type","page":"Messages","title":"Abstract message type","text":"","category":"section"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"Both belief propagation and variational messages are subtypes of a AbstractMessage supertype.","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"AbstractMessage","category":"page"},{"location":"lib/message/#ReactiveMP.AbstractMessage","page":"Messages","title":"ReactiveMP.AbstractMessage","text":"AbstractMessage\n\nAn abstract supertype for all concrete message types.\n\nSee also: Message\n\n\n\n\n\n","category":"type"},{"location":"lib/message/#lib-belief-propagation-message","page":"Messages","title":"Belief-Propagation (or Sum-Product) message","text":"","category":"section"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"(Image: message) Belief propagation message","category":"page"},{"location":"lib/message/#lib-variational-message","page":"Messages","title":"Variational message","text":"","category":"section"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"(Image: message) Variational message with structured factorisation q(x, y)q(z) assumption","category":"page"},{"location":"lib/message/#Message-type","page":"Messages","title":"Message type","text":"","category":"section"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"All messages are encoded with the type Message. ","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"Message\nReactiveMP.materialize!","category":"page"},{"location":"lib/message/#ReactiveMP.Message","page":"Messages","title":"ReactiveMP.Message","text":"Message{D, A} <: AbstractMessage\n\nMessage structure encodes a Belief Propagation message, which holds some data that usually a probability distribution, but can also be an arbitrary object. Message acts as a proxy structure to data object and proxies most of the statistical functions, e.g. mean, mode, cov etc.\n\nArguments\n\ndata::D: message always holds some data object associated with it\nis_clamped::Bool, specifies if this message is clamped\nis_initial::Bool, specifies if this message is initial\naddons::A, specifies the addons of the message\n\nExample\n\njulia> distribution = Gamma(10.0, 2.0)\nDistributions.Gamma{Float64}(α=10.0, θ=2.0)\n\njulia> message = Message(distribution, false, true, nothing)\nMessage(Distributions.Gamma{Float64}(α=10.0, θ=2.0))\n\njulia> mean(message) \n20.0\n\njulia> getdata(message)\nDistributions.Gamma{Float64}(α=10.0, θ=2.0)\n\njulia> is_clamped(message)\nfalse\n\njulia> is_initial(message)\ntrue\n\n\nSee also: AbstractMessage, ReactiveMP.materialize!\n\n\n\n\n\n","category":"type"},{"location":"lib/message/#ReactiveMP.materialize!","page":"Messages","title":"ReactiveMP.materialize!","text":"materialize!(message::AbstractMessage)\n\nMaterializes an abstract message and converts it to be of type Message.\n\nSee also: Message\n\n\n\n\n\n","category":"function"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"From an implementation point a view the Message structure does nothing but hold some data object and redirects most of the statistical related functions to that data object. However, this object is used extensively in Julia's multiple dispatch. Our implementation also uses extra is_initial and is_clamped fields to determine if product of two messages results in is_initial or is_clamped posterior marginal. The final field contains the addons. These contain additional information on top of the functional form of the distribution, such as its scaling or computation history.","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"using ReactiveMP, BayesBase, Distributions, ExponentialFamily","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"distribution = NormalMeanPrecision(0.0, 1.0)\nmessage = Message(distribution, false, true, nothing)","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"mean(message), precision(message)","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"logpdf(message, 1.0)","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"is_clamped(message), is_initial(message)","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"The user should not really interact with Message structure while working with ReactiveMP unless doing some advanced inference procedures that involve prediction.","category":"page"},{"location":"lib/nodes/nodes/#lib-node","page":"Overview","title":"Nodes implementation","text":"","category":"section"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"In the message passing framework, one of the most important concepts is a factor node. A factor node represents a local function in a factorised representation of a generative model.","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"note: Note\nTo quickly check the list of all available factor nodes that can be used in the model specification language, call ?make_node or Base.doc(make_node).","category":"page"},{"location":"lib/nodes/nodes/#lib-custom-node","page":"Overview","title":"Adding a custom node","text":"","category":"section"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"ReactiveMP.jl exports the @node macro that allows for quick definition of a factor node with a fixed number of edges. The interface is the following:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"struct MyNewCustomNode end\n\n@node MyNewCustomNode Stochastic [ x, y, z ]\n# ^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^ ^^^^^^^^^^^\n# Node's tag/name Node's type A fixed set of edges\n# Another possible The very first edge (in this example `x`) is considered\n# value is to be the output of the node\n# `Deterministic`","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"This expression registers a new node that can be used with the inference engine. Note howeve, that the @node macro does not generate any message passing update rules. These must be defined using the @rule macro.","category":"page"},{"location":"lib/nodes/nodes/#lib-node-types","page":"Overview","title":"Node types","text":"","category":"section"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"We distinguish different types of factor nodes in order to have better control over Bethe Free Energy computation. Each factor node has either the Deterministic or Stochastic functional form type.","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"Deterministic\nStochastic\nisdeterministic\nisstochastic\nsdtype","category":"page"},{"location":"lib/nodes/nodes/#ReactiveMP.Deterministic","page":"Overview","title":"ReactiveMP.Deterministic","text":"Deterministic\n\nDeterministic object used to parametrize factor node object with determinstic type of relationship between variables.\n\nSee also: Stochastic, isdeterministic, isstochastic, sdtype\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.Stochastic","page":"Overview","title":"ReactiveMP.Stochastic","text":"Stochastic\n\nStochastic object used to parametrize factor node object with stochastic type of relationship between variables.\n\nSee also: Deterministic, isdeterministic, isstochastic, sdtype\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.isdeterministic","page":"Overview","title":"ReactiveMP.isdeterministic","text":"isdeterministic(node)\n\nFunction used to check if factor node object is deterministic or not. Returns true or false.\n\nSee also: Deterministic, Stochastic, isstochastic, sdtype\n\n\n\n\n\n","category":"function"},{"location":"lib/nodes/nodes/#ReactiveMP.isstochastic","page":"Overview","title":"ReactiveMP.isstochastic","text":"isstochastic(node)\n\nFunction used to check if factor node object is stochastic or not. Returns true or false.\n\nSee also: Deterministic, Stochastic, isdeterministic, sdtype\n\n\n\n\n\n","category":"function"},{"location":"lib/nodes/nodes/#ReactiveMP.sdtype","page":"Overview","title":"ReactiveMP.sdtype","text":"sdtype(object)\n\nReturns either Deterministic or Stochastic for a given object (if defined).\n\nSee also: Deterministic, Stochastic, isdeterministic, isstochastic\n\n\n\n\n\n","category":"function"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"using ReactiveMP, BayesBase, Distributions, ExponentialFamily","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"For example the + node has the Deterministic type:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"plus_node = make_node(+)\n\nprintln(\"Is `+` node deterministic: \", isdeterministic(plus_node))\nprintln(\"Is `+` node stochastic: \", isstochastic(plus_node))\nnothing #hide","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"On the other hand, the Bernoulli node has the Stochastic type:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"bernoulli_node = make_node(Bernoulli)\n\nprintln(\"Is `Bernoulli` node deterministic: \", isdeterministic(bernoulli_node))\nprintln(\"Is `Bernoulli` node stochastic: \", isstochastic(bernoulli_node))","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"To get an actual instance of the type object we use sdtype function:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"println(\"sdtype() of `+` node is \", sdtype(plus_node))\nprintln(\"sdtype() of `Bernoulli` node is \", sdtype(bernoulli_node))\nnothing #hide","category":"page"},{"location":"lib/nodes/nodes/#lib-node-functional-dependencies-pipeline","page":"Overview","title":"Node functional dependencies pipeline","text":"","category":"section"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"The generic implementation of factor nodes in ReactiveMP supports custom functional dependency pipelines. Briefly, the functional dependencies pipeline defines what dependencies are need to compute a single message. As an example, consider the belief-propagation message update equation for a factor node f with three edges: x, y and z:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"mu(x) = int mu(y) mu(z) f(x y z) mathrmdy mathrmdz","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"Here we see that in the standard setting for the belief-propagation message out of edge x, we need only messages from the edges y and z. In contrast, consider the variational message update rule equation with mean-field assumption:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"mu(x) = exp int q(y) q(z) log f(x y z) mathrmdy mathrmdz","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"We see that in this setting, we do not need messages mu(y) and mu(z), but only the marginals q(y) and q(z). The purpose of a functional dependencies pipeline is to determine functional dependencies (a set of messages or marginals) that are needed to compute a single message. By default, ReactiveMP.jl uses so-called DefaultFunctionalDependencies that correctly implements belief-propagation and variational message passing schemes (including both mean-field and structured factorisations). The full list of built-in pipelines is presented below:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"ReactiveMP.DefaultFunctionalDependencies\nReactiveMP.RequireMessageFunctionalDependencies\nReactiveMP.RequireMarginalFunctionalDependencies\nReactiveMP.RequireEverythingFunctionalDependencies","category":"page"},{"location":"lib/nodes/nodes/#ReactiveMP.DefaultFunctionalDependencies","page":"Overview","title":"ReactiveMP.DefaultFunctionalDependencies","text":"DefaultFunctionalDependencies\n\nThis pipeline translates directly to enforcing a variational message passing scheme. In order to compute a message out of some edge, this pipeline requires messages from edges within the same edge-cluster and marginals over other edge-clusters.\n\nSee also: ReactiveMP.RequireMessageFunctionalDependencies, ReactiveMP.RequireMarginalFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.RequireMessageFunctionalDependencies","page":"Overview","title":"ReactiveMP.RequireMessageFunctionalDependencies","text":"RequireMessageFunctionalDependencies(indices::Tuple, start_with::Tuple)\n\nThe same as DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the inbound message on the this edge.\n\nArguments\n\nindices::Tuple, tuple of integers, which indicates what edges should require inbound messages\nstart_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial inbound messages for edges in indices\n\nNote: start_with uses setmessage! mechanism, hence, it can be visible by other listeners on the same edge. Explicit call to setmessage! overwrites whatever has been passed in start_with.\n\n@model macro accepts a simplified construction of this pipeline:\n\n@model function some_model()\n # ...\n y ~ NormalMeanVariance(x, τ) where {\n pipeline = RequireMessage(x = vague(NormalMeanPrecision), τ)\n # ^^^ ^^^\n # request 'inbound' for 'x' we may do the same for 'τ',\n # and initialise with `vague(...)` but here we skip initialisation\n }\n # ...\nend\n\nDeprecation warning: RequireInboundFunctionalDependencies has been deprecated in favor of RequireMessageFunctionalDependencies.\n\nSee also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMarginalFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.RequireMarginalFunctionalDependencies","page":"Overview","title":"ReactiveMP.RequireMarginalFunctionalDependencies","text":"RequireMarginalFunctionalDependencies(indices::Tuple, start_with::Tuple)\n\nSimilar to DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the posterior marginal on that edge.\n\nArguments\n\nindices::Tuple, tuple of integers, which indicates what edges should require their own marginals\nstart_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial marginal for edges in indices\n\nNote: start_with uses the setmarginal! mechanism, hence it can be visible to other listeners on the same edge. Explicit calls to setmarginal! overwrites whatever has been passed in start_with.\n\n@model macro accepts a simplified construction of this pipeline:\n\n@model function some_model()\n # ...\n y ~ NormalMeanVariance(x, τ) where {\n pipeline = RequireMarginal(x = vague(NormalMeanPrecision), τ)\n # ^^^ ^^^\n # request 'marginal' for 'x' we may do the same for 'τ',\n # and initialise with `vague(...)` but here we skip initialisation\n }\n # ...\nend\n\nNote: The simplified construction in @model macro syntax is only available in GraphPPL.jl of version >2.2.0.\n\nSee also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMessageFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.RequireEverythingFunctionalDependencies","page":"Overview","title":"ReactiveMP.RequireEverythingFunctionalDependencies","text":"RequireEverythingFunctionalDependencies\n\nThis pipeline specifies that in order to compute a message of some edge update rules request everything that is available locally. This includes all inbound messages (including on the same edge) and marginals over all local edge-clusters (this may or may not include marginals on single edges, depends on the local factorisation constraint).\n\nSee also: DefaultFunctionalDependencies, RequireMessageFunctionalDependencies, RequireMarginalFunctionalDependencies\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#lib-node-traits","page":"Overview","title":"Node traits","text":"","category":"section"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"Each factor node has to define the ReactiveMP.as_node_functional_form trait function and to specify a ReactiveMP.ValidNodeFunctionalForm singleton as a return object. By default ReactiveMP.as_node_functional_form returns ReactiveMP.UndefinedNodeFunctionalForm. Objects that do not specify this property correctly cannot be used in model specification.","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"note: Note\n@node macro does that automatically","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"ReactiveMP.ValidNodeFunctionalForm\nReactiveMP.UndefinedNodeFunctionalForm\nReactiveMP.as_node_functional_form","category":"page"},{"location":"lib/nodes/nodes/#ReactiveMP.ValidNodeFunctionalForm","page":"Overview","title":"ReactiveMP.ValidNodeFunctionalForm","text":"ValidNodeFunctionalForm\n\nTrait specification for an object that can be used in model specification as a factor node.\n\nSee also: ReactiveMP.as_node_functional_form, ReactiveMP.UndefinedNodeFunctionalForm\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.UndefinedNodeFunctionalForm","page":"Overview","title":"ReactiveMP.UndefinedNodeFunctionalForm","text":"UndefinedNodeFunctionalForm\n\nTrait specification for an object that can not be used in model specification as a factor node.\n\nSee also: ReactiveMP.as_node_functional_form, ReactiveMP.ValidNodeFunctionalForm\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.as_node_functional_form","page":"Overview","title":"ReactiveMP.as_node_functional_form","text":"as_node_functional_form(object)\n\nDetermines object node functional form trait specification. Returns either ValidNodeFunctionalForm() or UndefinedNodeFunctionalForm().\n\nSee also: ReactiveMP.ValidNodeFunctionalForm, ReactiveMP.UndefinedNodeFunctionalForm\n\n\n\n\n\n","category":"function"},{"location":"lib/nodes/flow/#lib-nodes-flow","page":"Flow","title":"Flow node","text":"","category":"section"},{"location":"lib/nodes/flow/","page":"Flow","title":"Flow","text":"See also Flow tutorial for a comprehensive guide on using flows in ReactiveMP.","category":"page"},{"location":"lib/nodes/flow/","page":"Flow","title":"Flow","text":"PlanarFlow\nRadialFlow\nFlowModel\nCompiledFlowModel\ncompile\nAdditiveCouplingLayer\nInputLayer\nPermutationLayer\nFlowMeta","category":"page"},{"location":"lib/nodes/flow/#ReactiveMP.PlanarFlow","page":"Flow","title":"ReactiveMP.PlanarFlow","text":"The PlanarFlow function is defined as\n\nf(bfx) = bfx + bfu tanh(bfw^top bfx + b)\n\nwith input and output dimension D. Here bfxin mathbbR^D represents the input of the function. Furthermore bfuin mathbbR^D, bfwin mathbbR^D and binmathbbR represent the parameters of the function. The function contracts and expands the input space. \n\nThis function has been introduced in:\n\nRezende, Danilo, and Shakir Mohamed. \"Variational inference with normalizing flows.\" International conference on machine learning. PMLR, 2015.\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.RadialFlow","page":"Flow","title":"ReactiveMP.RadialFlow","text":"The RadialFlow function is defined as\n\nf(bfx) = bfx + fracbeta(bfz - bfz_0)alpha + bfz - bfz_0\n\nwith input and output dimension D. Here bfxin mathbbR^D represents the input of the function. Furthermore bfz_0in mathbbR^D, alphain mathbbR and betainmathbbR represent the parameters of the function. The function contracts and expands the input space. \n\nThis function has been introduced in:\n\nRezende, Danilo, and Shakir Mohamed. \"Variational inference with normalizing flows.\" International conference on machine learning. PMLR, 2015.\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.FlowModel","page":"Flow","title":"ReactiveMP.FlowModel","text":"The FlowModel structure is the most generic type of Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimensionality and a tuple of layers and can be constructed as FlowModel( dim, (layer1, layer2, ...) ).\n\nNote: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.CompiledFlowModel","page":"Flow","title":"ReactiveMP.CompiledFlowModel","text":"The CompiledFlowModel structure is the most generic type of compiled Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimension and a tuple of compiled layers. Do not manually create a CompiledFlowModel! Instead create a FlowModel first and compile it with compile(model::FlowModel). This will make sure that all layers/mappings are configured with the proper dimensionality and with randomly sampled parameters. Alternatively, if you would like to pass your own parameters, call compile(model::FlowModel, params::Vector).\n\nNote: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.compile","page":"Flow","title":"ReactiveMP.compile","text":"compile() compiles a model by setting its parameters. It randomly sets parameter values in the layers and flows such that inference in the model can be obtained.\n\nInput arguments\n\nmodel::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.\n\nReturn arguments\n\n::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.\n\n\n\n\n\ncompile(model::FlowModel, params::Vector) lets you initialize a model model with a vector of parameters params.\n\nInput arguments\n\nmodel::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.\nparams::Vector - a vector of parameters with which the model should be compiled.\n\nReturn arguments\n\n::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.\n\n\n\n\n\n","category":"function"},{"location":"lib/nodes/flow/#ReactiveMP.AdditiveCouplingLayer","page":"Flow","title":"ReactiveMP.AdditiveCouplingLayer","text":"The additive coupling layer specifies an invertible function bfy = g(bfx) following the specific structure (for the mapping g mathbbR^2 rightarrow mathbbR^2):\n\n beginalign\n y_1 = x_1 \n y_2 = x_2 + f(x_1)\n endalign\n\nwhere f(cdot) denotes an arbitrary function with mapping f mathbbR rightarrow mathbbR. This function can be chosen arbitrarily complex. Non-linear functions (neural networks) are often chosen to model complex relationships. From the definition of the model, invertibility can be easily achieved as\n\n beginalign\n x_1 = y_1 \n x_2 = y_2 - f(y_1)\n endalign\n\nThe current implementation only allows for the mapping g mathbbR^2 rightarrow mathbbR^2, although this layer can be generalized for arbitrary input dimensions.\n\nAdditiveCouplingLayer(f <: AbstractCouplingFlow) creates the layer structure with function f.\n\nExample\n\nf = PlanarFlow()\nlayer = AdditiveCouplingLayer(f)\n\nThis layer structure has been introduced in:\n\nDinh, Laurent, David Krueger, and Yoshua Bengio. \"Nice: Non-linear independent components estimation.\" arXiv preprint arXiv:1410.8516 (2014).\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.InputLayer","page":"Flow","title":"ReactiveMP.InputLayer","text":"The input layer specifies the input dimension to a flow model.\n\nlayer = InputLayer(3)\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.PermutationLayer","page":"Flow","title":"ReactiveMP.PermutationLayer","text":"The permutation layer specifies an invertible mapping bfy = g(bfx) = Pbfx where P is a permutation matrix.\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.FlowMeta","page":"Flow","title":"ReactiveMP.FlowMeta","text":"The FlowMeta structure contains the meta data of the Flow factor node. More specifically, it contains the model of the Flow factor node. The FlowMeta structure can be constructed as FlowMeta(model). Make sure that the flow model has been compiled.\n\nThe FlowMeta structure is required for the Flow factor node and can be included with the Flow node as: y ~ Flow(x) where { meta = FlowMeta(...) }\n\n\n\n\n\n","category":"type"},{"location":"extra/contributing/#Contribution-guidelines","page":"Contributing","title":"Contribution guidelines","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We welcome all possible contributors. This page details the some of the guidelines that should be followed when contributing to this package.","category":"page"},{"location":"extra/contributing/#Reporting-bugs","page":"Contributing","title":"Reporting bugs","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We track bugs using GitHub issues. We encourage you to write complete, specific, reproducible bug reports. Mention the versions of Julia and ReactiveMP for which you observe unexpected behavior. Please provide a concise description of the problem and complement it with code snippets, test cases, screenshots, tracebacks or any other information that you consider relevant. This will help us to replicate the problem and narrow the search space for solutions.","category":"page"},{"location":"extra/contributing/#Suggesting-features","page":"Contributing","title":"Suggesting features","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We welcome new feature proposals. However, before submitting a feature request, consider a few things:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"Does the feature require changes in the core ReactiveMP.jl code? If it doesn't (for example, you would like to add a factor node for a particular application), you can add local extensions in your script/notebook or consider making a separate repository for your extensions.\nIf you would like to add an implementation of a feature that changes a lot in the core ReactiveMP.jl code, please open an issue on GitHub and describe your proposal first. This will allow us to discuss your proposal with you before you invest your time in implementing something that may be difficult to merge later on.","category":"page"},{"location":"extra/contributing/#Contributing-code","page":"Contributing","title":"Contributing code","text":"","category":"section"},{"location":"extra/contributing/#Installing-ReactiveMP","page":"Contributing","title":"Installing ReactiveMP","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We suggest that you use the dev command from the new Julia package manager to install ReactiveMP.jl for development purposes. To work on your fork of ReactiveMP.jl, use your fork's URL address in the dev command, for example:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"] dev git@github.com:your_username/ReactiveMP.jl.git","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"The dev command clones ReactiveMP.jl to ~/.julia/dev/ReactiveMP. All local changes to ReactiveMP code will be reflected in imported code.","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"note: Note\nIt is also might be useful to install Revise.jl package as it allows you to modify code and use the changes without restarting Julia.","category":"page"},{"location":"extra/contributing/#Committing-code","page":"Contributing","title":"Committing code","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We use the standard GitHub Flow workflow where all contributions are added through pull requests. In order to contribute, first fork the repository, then commit your contributions to your fork, and then create a pull request on the master branch of the ReactiveMP.jl repository.","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"Before opening a pull request, please make sure that all tests pass without failing! All demos (can be found in /demo/ directory) and benchmarks (can be found in /benchmark/ directory) have to run without errors as well.","category":"page"},{"location":"extra/contributing/#Style-conventions","page":"Contributing","title":"Style conventions","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"note: Note\nReactiveMP.jl repository contains scripts to automatically format code according to our guidelines. Use make format command to fix code style. This command overwrites files.","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We use default Julia style guide. We list here a few important points and our modifications to the Julia style guide:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"Use 4 spaces for indentation\nType names use UpperCamelCase. For example: AbstractFactorNode, RandomVariable, etc..\nFunction names are lowercase with underscores, when necessary. For example: activate!, randomvar, as_variable, etc..\nVariable names and function arguments use snake_case\nThe name of a method that modifies its argument(s) must end in !","category":"page"},{"location":"extra/contributing/#Unit-tests","page":"Contributing","title":"Unit tests","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We use the test-driven development (TDD) methodology for ReactiveMP.jl development. The test coverage should be as complete as possible. Please make sure that you write tests for each piece of code that you want to add.","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"All unit tests are located in the /test/ directory. The /test/ directory follows the structure of the /src/ directory. Each test file should have following filename format: test_*.jl. Some tests are also present in jldoctest docs annotations directly in the source code. See Julia's documentation about doctests.","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"The tests can be evaluated by running following command in the Julia REPL:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"] test ReactiveMP","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"In addition tests can be evaluated by running following command in the ReactiveMP root directory:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"make test","category":"page"},{"location":"extra/contributing/#Fixes-to-external-libraries","page":"Contributing","title":"Fixes to external libraries","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"If a bug has been discovered in an external dependencies of the ReactiveMP.jl it is the best to open an issue directly in the dependency's github repository. You use can use the fixes.jl file for hot-fixes before a new release of the broken dependecy is available.","category":"page"},{"location":"extra/contributing/#Makefile","page":"Contributing","title":"Makefile","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"ReactiveMP.jl uses Makefile for most common operations:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"make help: Shows help snippet\nmake test: Run tests, supports extra arguments\nmake test test_args=\"distributions:normal_mean_variance\" would run tests only from distributions/test_normal_mean_variance.jl\nmake test test_args=\"distributions:normal_mean_variance models:lgssm\" would run tests both from distributions/test_normal_mean_variance.jl and models/test_lgssm.jl\nmake docs: Compile documentation\nmake benchmark: Run simple benchmark\nmake lint: Check codestyle\nmake format: Check and fix codestyle ","category":"page"},{"location":"extra/extensions/#Extensions-and-interaction-with-the-Julia-ecosystem","page":"Extensions and interaction with the Julia ecosystem","title":"Extensions and interaction with the Julia ecosystem","text":"","category":"section"},{"location":"extra/extensions/","page":"Extensions and interaction with the Julia ecosystem","title":"Extensions and interaction with the Julia ecosystem","text":"ReactiveMP.jl exports extra functionality if other Julia packages are loaded in the same environment.","category":"page"},{"location":"extra/extensions/#Optimisers.jl","page":"Extensions and interaction with the Julia ecosystem","title":"Optimisers.jl","text":"","category":"section"},{"location":"extra/extensions/","page":"Extensions and interaction with the Julia ecosystem","title":"Extensions and interaction with the Julia ecosystem","text":"The Optimizers.jl package defines many standard gradient-based optimisation rules, and tools for applying them to deeply nested models. The optimizers defined in the Optimziers.jl are compatible with the CVI approximation method.","category":"page"},{"location":"extra/extensions/#DiffResults.jl-(loaded-automatically-with-the-ForwardDiff.jl)","page":"Extensions and interaction with the Julia ecosystem","title":"DiffResults.jl (loaded automatically with the ForwardDiff.jl)","text":"","category":"section"},{"location":"extra/extensions/","page":"Extensions and interaction with the Julia ecosystem","title":"Extensions and interaction with the Julia ecosystem","text":"The DiffResults.jl provides the DiffResult type, which can be passed to in-place differentiation methods instead of an output buffer. If loaded in the current Julia session enables faster derivatives with the ForwardDiffGrad option in the CVI approximation method (in the Gaussian case).","category":"page"},{"location":"custom/custom-functional-form/#custom-functional-form","page":"Custom functional form","title":"Custom Functional Form Specification","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"In a nutshell, functional form constraints defines a function that approximates the product of colliding messages and computes posterior marginal that can be used later on during the inference procedure. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. For example, if we refer to our CustomFunctionalForm as to f we can see the whole functional form constraints pipeline as:","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"q(x) = fleft(fracoverrightarrowmu(x)overleftarrowmu(x)int overrightarrowmu(x)overleftarrowmu(x) mathrmdxright)","category":"page"},{"location":"custom/custom-functional-form/#Interface","page":"Custom functional form","title":"Interface","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"ReactiveMP.jl, however, uses some extra utility functions to define functional form constraint behaviour. Here we briefly describe all utility function. If you are only interested in the concrete example, you may directly head to the Custom Functional Form example at the end of this section.","category":"page"},{"location":"custom/custom-functional-form/#Abstract-super-type","page":"Custom functional form","title":"Abstract super type","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"AbstractFormConstraint\nCompositeFormConstraint","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.AbstractFormConstraint","page":"Custom functional form","title":"ReactiveMP.AbstractFormConstraint","text":"AbstractFormConstraint\n\nEvery functional form constraint is a subtype of AbstractFormConstraint abstract type.\n\nNote: this is not strictly necessary, but it makes automatic dispatch easier and compatible with the CompositeFormConstraint.\n\nSee also: CompositeFormConstraint\n\n\n\n\n\n","category":"type"},{"location":"custom/custom-functional-form/#ReactiveMP.CompositeFormConstraint","page":"Custom functional form","title":"ReactiveMP.CompositeFormConstraint","text":"CompositeFormConstraint\n\nCreates a composite form constraint that applies form constraints in order. The composed form constraints must be compatible and have the exact same form_check_strategy. Any functional form constraint that defines is_point_mass_form_constraint() = true may be used only as the last element of the composition.\n\n\n\n\n\n","category":"type"},{"location":"custom/custom-functional-form/#Form-check-strategy","page":"Custom functional form","title":"Form check strategy","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"Every custom functional form must implement a new method for the default_form_check_strategy function that returns either FormConstraintCheckEach or FormConstraintCheckLast.","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"FormConstraintCheckLast: q(x) = f(μ1(x) * μ2(x) * μ3(x))\nFormConstraintCheckEach: q(x) = f(f(μ1(x) * μ2(x)) * μ3(x))","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"default_form_check_strategy\nFormConstraintCheckEach\nFormConstraintCheckLast","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.default_form_check_strategy","page":"Custom functional form","title":"ReactiveMP.default_form_check_strategy","text":"default_form_check_strategy(form_constraint)\n\nReturns a default check strategy (e.g. FormConstraintCheckEach or FormConstraintCheckEach) for a given form constraint object.\n\nSee also: FormConstraintCheckEach, FormConstraintCheckLast, constrain_form\n\n\n\n\n\n","category":"function"},{"location":"custom/custom-functional-form/#ReactiveMP.FormConstraintCheckEach","page":"Custom functional form","title":"ReactiveMP.FormConstraintCheckEach","text":"FormConstraintCheckEach\n\nThis form constraint check strategy checks functional form of the messages product after each product in an equality chain. Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed after each subsequent prod function.\n\nSee also: FormConstraintCheckLast, default_form_check_strategy, constrain_form\n\n\n\n\n\n","category":"type"},{"location":"custom/custom-functional-form/#ReactiveMP.FormConstraintCheckLast","page":"Custom functional form","title":"ReactiveMP.FormConstraintCheckLast","text":"FormConstraintCheckEach\n\nThis form constraint check strategy checks functional form of the last messages product in the equality chain. Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed only once after all subsequenct prod functions have been executed.\n\nSee also: FormConstraintCheckLast, default_form_check_strategy, constrain_form\n\n\n\n\n\n","category":"type"},{"location":"custom/custom-functional-form/#Prod-constraint","page":"Custom functional form","title":"Prod constraint","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"Every custom functional form must implement a new method for the default_prod_constraint function that returns a proper prod_constraint object.","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"default_prod_constraint","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.default_prod_constraint","page":"Custom functional form","title":"ReactiveMP.default_prod_constraint","text":"default_prod_constraint(form_constraint)\n\nReturns a default prod constraint needed to apply a given form_constraint. For most form constraints this function returns ProdGeneric.\n\n\n\n\n\n","category":"function"},{"location":"custom/custom-functional-form/#Constrain-form,-a.k.a-f","page":"Custom functional form","title":"Constrain form, a.k.a f","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"The main function that a custom functional form must implement, which we referred to as f in the beginning of this section, is the constrain_form function.","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"constrain_form","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.constrain_form","page":"Custom functional form","title":"ReactiveMP.constrain_form","text":"constrain_form(form_constraint, distribution)\n\nThis function must approximate distribution object in a form that satisfies form_constraint.\n\nSee also: FormConstraintCheckEach, FormConstraintCheckLast, default_form_check_strategy, is_point_mass_form_constraint\n\n\n\n\n\n","category":"function"},{"location":"custom/custom-functional-form/#Is-point-mass-form-constraint-(optional)","page":"Custom functional form","title":"Is point mass form constraint (optional)","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"Every custom functional form may implement a new method for the is_point_mass_form_constraint function that returns either true or false. This is an utility function that simplifes computation of the Bethe Free Energy and is not strictly necessary.","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"is_point_mass_form_constraint","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.is_point_mass_form_constraint","page":"Custom functional form","title":"ReactiveMP.is_point_mass_form_constraint","text":"is_point_mass_form_constraint(form_constraint)\n\nSpecifies whether form constraint always returns PointMass estimates or not. For a given form_constraint returns either true or false.\n\nSee also: FormConstraintCheckEach, FormConstraintCheckLast, constrain_form\n\n\n\n\n\n","category":"function"},{"location":"custom/custom-functional-form/#Compatibility-with-@constraints-macro-(optional)","page":"Custom functional form","title":"Compatibility with @constraints macro (optional)","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"To make custom functional form constraint compatible with the @constraints macro, it must implement a new method for the make_form_constraint function.","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"make_form_constraint","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.make_form_constraint","page":"Custom functional form","title":"ReactiveMP.make_form_constraint","text":"make_form_constraint(::Type, args...; kwargs...)\n\nCreates form constraint object based on passed type with given args and kwargs. Used to simplify form constraint specification.\n\nAs an example:\n\nmake_form_constraint(PointMass)\n\ncreates an instance of PointMassFormConstraint and \n\nmake_form_constraint(SampleList, 5000, LeftProposal())\n\nshould create an instance of SampleListFormConstraint.\n\nSee also: AbstractFormConstraint\n\n\n\n\n\n","category":"function"},{"location":"custom/custom-functional-form/#custom-functional-form-example","page":"Custom functional form","title":"Custom Functional Form Example","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"In this demo we show how to build a custom functional form constraint that is compatible with the ReactiveMP.jl inference backend. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. We show a relatively simple use-case, which might not be very useful in practice, but serves as a simple step-by-step guide. Assume that we want a specific posterior marginal of some random variable in our model to have a specific Gaussian parametrisation, for example mean-precision. We can use built-in NormalMeanPrecision distribution, but we still need to define our custom functional form constraint:","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"using ReactiveMP, BayesBase\n\n# First we define our functional form structure with no fields\nstruct MeanPrecisionFormConstraint <: AbstractFormConstraint end","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"Next we define the behaviour of our functional form constraint:","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"ReactiveMP.is_point_mass_form_constraint(::MeanPrecisionFormConstraint) = false\nReactiveMP.default_form_check_strategy(::MeanPrecisionFormConstraint) = FormConstraintCheckLast()\nReactiveMP.default_prod_constraint(::MeanPrecisionFormConstraint) = GenericProd()\n\nfunction ReactiveMP.constrain_form(::MeanPrecisionFormConstraint, distribution) \n # This is quite a naive assumption, that a given `dsitribution` object has `mean` and `precision` defined\n # However this quantities might be approximated with some other external method, e.g. Laplace approximation\n m = mean(distribution) # or approximate with some other method\n p = precision(distribution) # or approximate with some other method\n return NormalMeanPrecision(m, p)\nend\n\nfunction ReactiveMP.constrain_form(::MeanPrecisionFormConstraint, distribution::BayesBase.ProductOf)\n # ProductOf is the special case, read about this type more in the corresponding documentation section\n # of the `BayesBase` package\n # ... \nend","category":"page"},{"location":"custom/custom-addons/#custom-addons","page":"Custom addons","title":"Custom Addons","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"Standard message passing schemes only pass along distributions to other nodes. However, for more advanced usage, there might be a need for passing along additional information in messages and/or marginals. One can for example think of passing along the scaling of the distribution or some information that specifies how the message or marginal was computed, i.e. which messages were used for its computation and which node was preceding it. Another use cases is saving extra debugging information inside messages themselves, e.g. what arguments have been used to compute a message.","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"Addons provide a solution here. Basically, addons are structures that contain extra information that are passed along the graph with messages and marginals in a tuple. These addons can be extracted using the getaddons(message/marginal) function. Its usage and operations can differ significantly for each application, yet below gives a concise overview on how to implement them on your own.","category":"page"},{"location":"custom/custom-addons/#Example","page":"Custom addons","title":"Example","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"Suppose that we wish to create an addon that counts the number of computations that preceded some message or marginal. This addon can be created by adding the file src/addons/count.jl and by including it in the ReactiveMP.jl file.","category":"page"},{"location":"custom/custom-addons/#Step-1:-Creating-the-addon-structure","page":"Custom addons","title":"Step 1: Creating the addon structure","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"Let's start by defining our new addon structure. This might seem daunting, but basically only requires us to specify the information that we would like to collect. Just make sure that it is specified as a subtype of AbstractAddon. In our example this becomes:","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"struct AddonCount{T} <: AbstractAddon\n count :: T\nend","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"You can add additional fields or functions for improved handling, such as get_count() or show() functions.","category":"page"},{"location":"custom/custom-addons/#Step-2:-Compute-addon-value-after-computing-a-message","page":"Custom addons","title":"Step 2: Compute addon value after computing a message","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"As a second step we need to specify how the addon behaves when a new message is computed in a factor node. For this purpose we need to implement a specialized version of the message_mapping_addon() function. This function accepts the mapping variables of the factor node and updates the addons by extending the tuple.","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"In our example we could write","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"# This specification assumes that the default value for addon is `AddonCount(nothing)`\nfunction message_mapping_addon(::AddonCount{Nothing}, mapping, messages, marginals, result, addons)\n\n # get number of operations of messages\n message_count = 0\n for message in messages\n message_count += getcount(message)\n end\n\n # get number of operations of marginals\n marginal_count = 0\n for marginal in marginals\n marginal_count += getcount(marginal)\n end\n\n # extend addons with AddonCount() structure\n return AddonCount(message_count + marginal_count + 1)\nend\n","category":"page"},{"location":"custom/custom-addons/#Step-3:-Computing-products","page":"Custom addons","title":"Step 3: Computing products","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"The goal is to update the AddonCount structure when we multiply 2 messages. As a result, we need to write a function that allows us to define this behaviour. This function is called multiply_addons and accepts 5 arguments. In our example this becomes","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"function multiply_addons(left_addon::AddonCount, right_addon::AddonCount, new_dist, left_dist, right_dist)\n return AddonCount(left_addon.count + right_addon.count + 1)\nend","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"here we add the number of operations from the addons that are being multiplied and we add one (for the current operation). we are aware that this is likely not valid for iterative message passing schemes, but it still serves as a nice example. the left_addon and right_addon argument specify the addoncount objects that are being multiplied. corresponding to these addons, there are the distributions left_dist and right_dist, which might contain information for computing the product. the new distribution new_dist ∝ left_dist * right_dist is also passed along for potentially reusing the result of earlier computations.","category":"page"},{"location":"custom/custom-addons/#More-information","page":"Custom addons","title":"More information","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"For more advanced information check the implementation of the log-scale or memory addons.","category":"page"},{"location":"lib/methods/#lib-using-methods","page":"Exported methods","title":"Using methods from ReactiveMP","text":"","category":"section"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"In the Julia programming language (in contrast to Python for example) the most common way of loading a module is:","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"using ReactiveMP","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"A nice explanation about how modules/packages work in Julia can be found in the official documentation.","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"In a nutshell, Julia automatically resolves all name collisions and there is no a lot of benefit of importing specific names, e.g.:","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"import ReactiveMP: mean","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"One of the reasons for that is that Julia uses multiple-dispatch capabilities to merge names automatically and will indicate (with a warning) if something went wrong or names have unresolvable collisions on types. As a small example of this feature consider the following small import example:","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"import ReactiveMP: mean as mean_from_reactivemp\nimport Distributions: mean as mean_from_distributions\nimport BayesBase: mean as mean_from_bayes_base","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"mean_from_reactivemp === mean_from_distributions","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"mean_from_reactivemp === mean_from_bayes_base","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"Even though we import mean function from different packages they actually refer to the same object. Worth noting that this is not always the case - Julia will print a warning in case it finds unresolvable conflicts and usage of such functions will be disallowed unless user import them specifically. Read more about this in the section of the Julia's documentation.","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"# It is easier to let Julia resolve names automatically\n# Julia will not overwrite `mean` that is coming from both packages\nusing ReactiveMP, Distributions, BayesBase, ExponentialFamily","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"mean(Normal(0.0, 1.0)) # `Normal` is an object from `Distributions.jl`","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"mean(NormalMeanVariance(0.0, 1.0)) # `NormalMeanVariance` is an object from `ReactiveMP.jl`","category":"page"},{"location":"lib/methods/#lib-list-methods","page":"Exported methods","title":"List of available methods","text":"","category":"section"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"Below you can find a list of exported methods from ReactiveMP.jl. All methods (even private) can be always accessed with ReactiveMP. prefix, e.g ReactiveMP.mean.","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"note: Note\nSome exported names are (for legacy reasons) intended for private usage only. As a result some of these methods do not have a proper associated documentation with them. We constantly improve ReactiveMP.jl library and continue to add better documentation for many exported methods, but a small portion of these methods could be removed from this list in the future.","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"using ReactiveMP #hide\nforeach(println, names(ReactiveMP))","category":"page"},{"location":"#ReactiveMP.jl","page":"Introduction","title":"ReactiveMP.jl","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"Julia package for reactive message passing Bayesian inference engine on a factor graph.","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"note: Note\nThis package exports only an inference engine, for the full ecosystem with convenient model and constraints specification we refer user to the RxInfer.jl package and its documentation.","category":"page"},{"location":"#Examples-and-tutorials","page":"Introduction","title":"Examples and tutorials","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"Tutorials and examples are available in the RxInfer documentation.","category":"page"},{"location":"#Table-of-Contents","page":"Introduction","title":"Table of Contents","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"Pages = [\n \"lib/message.md\",\n \"lib/node.md\",\n \"lib/math.md\",\n \"extra/contributing.md\"\n]\nDepth = 2","category":"page"},{"location":"#Index","page":"Introduction","title":"Index","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"","category":"page"},{"location":"lib/algebra/common/#lib-helpers-algebra-common","page":"Algebra utils","title":"Algebra common utilities","text":"","category":"section"},{"location":"lib/algebra/common/#lib-helpers-algebra-diageye","page":"Algebra utils","title":"diageye","text":"","category":"section"},{"location":"lib/algebra/common/","page":"Algebra utils","title":"Algebra utils","text":"diageye","category":"page"},{"location":"lib/algebra/common/#ReactiveMP.diageye","page":"Algebra utils","title":"ReactiveMP.diageye","text":"diageye(::Type{T}, n::Int)\n\nAn alias for the Matrix{T}(I, n, n). Returns a matrix of size n x n with ones (of type T) on the diagonal and zeros everywhere else.\n\n\n\n\n\ndiageye(n::Int)\n\nAn alias for the Matrix{Float64}(I, n, n). Returns a matrix of size n x n with ones (of type Float64) on the diagonal and zeros everywhere else.\n\n\n\n\n\n","category":"function"}] +} diff --git a/v3.13.2/siteinfo.js b/v3.13.2/siteinfo.js new file mode 100644 index 000000000..471608f94 --- /dev/null +++ b/v3.13.2/siteinfo.js @@ -0,0 +1 @@ +var DOCUMENTER_CURRENT_VERSION = "v3.13.2"; diff --git a/v3.13.3/.documenter-siteinfo.json b/v3.13.3/.documenter-siteinfo.json new file mode 100644 index 000000000..50bc8fa06 --- /dev/null +++ b/v3.13.3/.documenter-siteinfo.json @@ -0,0 +1 @@ +{"documenter":{"julia_version":"1.10.0","generation_timestamp":"2024-02-09T12:56:36","documenter_version":"1.2.1"}} \ No newline at end of file diff --git a/v3.13.3/assets/documenter.js b/v3.13.3/assets/documenter.js new file mode 100644 index 000000000..f5311607b --- /dev/null +++ b/v3.13.3/assets/documenter.js @@ -0,0 +1,889 @@ +// Generated by Documenter.jl +requirejs.config({ + paths: { + 'highlight-julia': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia.min', + 'headroom': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/headroom.min', + 'jqueryui': 'https://cdnjs.cloudflare.com/ajax/libs/jqueryui/1.13.2/jquery-ui.min', + 'minisearch': 'https://cdn.jsdelivr.net/npm/minisearch@6.1.0/dist/umd/index.min', + 'katex-auto-render': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/contrib/auto-render.min', + 'jquery': 'https://cdnjs.cloudflare.com/ajax/libs/jquery/3.7.0/jquery.min', + 'headroom-jquery': 'https://cdnjs.cloudflare.com/ajax/libs/headroom/0.12.0/jQuery.headroom.min', + 'katex': 'https://cdnjs.cloudflare.com/ajax/libs/KaTeX/0.16.8/katex.min', + 'highlight': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/highlight.min', + 'highlight-julia-repl': 'https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.8.0/languages/julia-repl.min', + }, + shim: { + "highlight-julia": { + "deps": [ + "highlight" + ] + }, + "katex-auto-render": { + "deps": [ + "katex" + ] + }, + "headroom-jquery": { + "deps": [ + "jquery", + "headroom" + ] + }, + "highlight-julia-repl": { + "deps": [ + "highlight" + ] + } +} +}); +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'katex', 'katex-auto-render'], function($, katex, renderMathInElement) { +$(document).ready(function() { + renderMathInElement( + document.body, + { + "delimiters": [ + { + "left": "$", + "right": "$", + "display": false + }, + { + "left": "$$", + "right": "$$", + "display": true + }, + { + "left": "\\[", + "right": "\\]", + "display": true + } + ] +} + + ); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'highlight', 'highlight-julia', 'highlight-julia-repl'], function($) { +$(document).ready(function() { + hljs.highlightAll(); +}) + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +let timer = 0; +var isExpanded = true; + +$(document).on("click", ".docstring header", function () { + let articleToggleTitle = "Expand docstring"; + + debounce(() => { + if ($(this).siblings("section").is(":visible")) { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + } else { + $(this) + .find(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + articleToggleTitle = "Collapse docstring"; + } + + $(this) + .find(".docstring-article-toggle-button") + .prop("title", articleToggleTitle); + $(this).siblings("section").slideToggle(); + }); +}); + +$(document).on("click", ".docs-article-toggle-button", function () { + let articleToggleTitle = "Expand docstring"; + let navArticleToggleTitle = "Expand all docstrings"; + + debounce(() => { + if (isExpanded) { + $(this).removeClass("fa-chevron-up").addClass("fa-chevron-down"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-down") + .addClass("fa-chevron-right"); + + isExpanded = false; + + $(".docstring section").slideUp(); + } else { + $(this).removeClass("fa-chevron-down").addClass("fa-chevron-up"); + $(".docstring-article-toggle-button") + .removeClass("fa-chevron-right") + .addClass("fa-chevron-down"); + + isExpanded = true; + articleToggleTitle = "Collapse docstring"; + navArticleToggleTitle = "Collapse all docstrings"; + + $(".docstring section").slideDown(); + } + + $(this).prop("title", navArticleToggleTitle); + $(".docstring-article-toggle-button").prop("title", articleToggleTitle); + }); +}); + +function debounce(callback, timeout = 300) { + if (Date.now() - timer > timeout) { + callback(); + } + + clearTimeout(timer); + + timer = Date.now(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require([], function() { +function addCopyButtonCallbacks() { + for (const el of document.getElementsByTagName("pre")) { + const button = document.createElement("button"); + button.classList.add("copy-button", "fa-solid", "fa-copy"); + button.setAttribute("aria-label", "Copy this code block"); + button.setAttribute("title", "Copy"); + + el.appendChild(button); + + const success = function () { + button.classList.add("success", "fa-check"); + button.classList.remove("fa-copy"); + }; + + const failure = function () { + button.classList.add("error", "fa-xmark"); + button.classList.remove("fa-copy"); + }; + + button.addEventListener("click", function () { + copyToClipboard(el.innerText).then(success, failure); + + setTimeout(function () { + button.classList.add("fa-copy"); + button.classList.remove("success", "fa-check", "fa-xmark"); + }, 5000); + }); + } +} + +function copyToClipboard(text) { + // clipboard API is only available in secure contexts + if (window.navigator && window.navigator.clipboard) { + return window.navigator.clipboard.writeText(text); + } else { + return new Promise(function (resolve, reject) { + try { + const el = document.createElement("textarea"); + el.textContent = text; + el.style.position = "fixed"; + el.style.opacity = 0; + document.body.appendChild(el); + el.select(); + document.execCommand("copy"); + + resolve(); + } catch (err) { + reject(err); + } finally { + document.body.removeChild(el); + } + }); + } +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", addCopyButtonCallbacks); +} else { + addCopyButtonCallbacks(); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'headroom', 'headroom-jquery'], function($, Headroom) { + +// Manages the top navigation bar (hides it when the user starts scrolling down on the +// mobile). +window.Headroom = Headroom; // work around buggy module loading? +$(document).ready(function () { + $("#documenter .docs-navbar").headroom({ + tolerance: { up: 10, down: 10 }, + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery', 'minisearch'], function($, minisearch) { + +// In general, most search related things will have "search" as a prefix. +// To get an in-depth about the thought process you can refer: https://hetarth02.hashnode.dev/series/gsoc + +let results = []; +let timer = undefined; + +let data = documenterSearchIndex["docs"].map((x, key) => { + x["id"] = key; // minisearch requires a unique for each object + return x; +}); + +// list below is the lunr 2.1.3 list minus the intersect with names(Base) +// (all, any, get, in, is, only, which) and (do, else, for, let, where, while, with) +// ideally we'd just filter the original list but it's not available as a variable +const stopWords = new Set([ + "a", + "able", + "about", + "across", + "after", + "almost", + "also", + "am", + "among", + "an", + "and", + "are", + "as", + "at", + "be", + "because", + "been", + "but", + "by", + "can", + "cannot", + "could", + "dear", + "did", + "does", + "either", + "ever", + "every", + "from", + "got", + "had", + "has", + "have", + "he", + "her", + "hers", + "him", + "his", + "how", + "however", + "i", + "if", + "into", + "it", + "its", + "just", + "least", + "like", + "likely", + "may", + "me", + "might", + "most", + "must", + "my", + "neither", + "no", + "nor", + "not", + "of", + "off", + "often", + "on", + "or", + "other", + "our", + "own", + "rather", + "said", + "say", + "says", + "she", + "should", + "since", + "so", + "some", + "than", + "that", + "the", + "their", + "them", + "then", + "there", + "these", + "they", + "this", + "tis", + "to", + "too", + "twas", + "us", + "wants", + "was", + "we", + "were", + "what", + "when", + "who", + "whom", + "why", + "will", + "would", + "yet", + "you", + "your", +]); + +let index = new minisearch({ + fields: ["title", "text"], // fields to index for full-text search + storeFields: ["location", "title", "text", "category", "page"], // fields to return with search results + processTerm: (term) => { + let word = stopWords.has(term) ? null : term; + if (word) { + // custom trimmer that doesn't strip @ and !, which are used in julia macro and function names + word = word + .replace(/^[^a-zA-Z0-9@!]+/, "") + .replace(/[^a-zA-Z0-9@!]+$/, ""); + } + + return word ?? null; + }, + // add . as a separator, because otherwise "title": "Documenter.Anchors.add!", would not find anything if searching for "add!", only for the entire qualification + tokenize: (string) => string.split(/[\s\-\.]+/), + // options which will be applied during the search + searchOptions: { + boost: { title: 100 }, + fuzzy: 2, + processTerm: (term) => { + let word = stopWords.has(term) ? null : term; + if (word) { + word = word + .replace(/^[^a-zA-Z0-9@!]+/, "") + .replace(/[^a-zA-Z0-9@!]+$/, ""); + } + + return word ?? null; + }, + tokenize: (string) => string.split(/[\s\-\.]+/), + }, +}); + +index.addAll(data); + +let filters = [...new Set(data.map((x) => x.category))]; +var modal_filters = make_modal_body_filters(filters); +var filter_results = []; + +$(document).on("keyup", ".documenter-search-input", function (event) { + // Adding a debounce to prevent disruptions from super-speed typing! + debounce(() => update_search(filter_results), 300); +}); + +$(document).on("click", ".search-filter", function () { + if ($(this).hasClass("search-filter-selected")) { + $(this).removeClass("search-filter-selected"); + } else { + $(this).addClass("search-filter-selected"); + } + + // Adding a debounce to prevent disruptions from crazy clicking! + debounce(() => get_filters(), 300); +}); + +/** + * A debounce function, takes a function and an optional timeout in milliseconds + * + * @function callback + * @param {number} timeout + */ +function debounce(callback, timeout = 300) { + clearTimeout(timer); + timer = setTimeout(callback, timeout); +} + +/** + * Make/Update the search component + * + * @param {string[]} selected_filters + */ +function update_search(selected_filters = []) { + let initial_search_body = ` +
Type something to get started!
+ `; + + let querystring = $(".documenter-search-input").val(); + + if (querystring.trim()) { + results = index.search(querystring, { + filter: (result) => { + // Filtering results + if (selected_filters.length === 0) { + return result.score >= 1; + } else { + return ( + result.score >= 1 && selected_filters.includes(result.category) + ); + } + }, + }); + + let search_result_container = ``; + let search_divider = `
`; + + if (results.length) { + let links = []; + let count = 0; + let search_results = ""; + + results.forEach(function (result) { + if (result.location) { + // Checking for duplication of results for the same page + if (!links.includes(result.location)) { + search_results += make_search_result(result, querystring); + count++; + } + + links.push(result.location); + } + }); + + let result_count = `
${count} result(s)
`; + + search_result_container = ` +
+ ${modal_filters} + ${search_divider} + ${result_count} +
+ ${search_results} +
+
+ `; + } else { + search_result_container = ` +
+ ${modal_filters} + ${search_divider} +
0 result(s)
+
+
No result found!
+ `; + } + + if ($(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").removeClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(search_result_container); + } else { + filter_results = []; + modal_filters = make_modal_body_filters(filters, filter_results); + + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".search-modal-card-body").html(initial_search_body); + } +} + +/** + * Make the modal filter html + * + * @param {string[]} filters + * @param {string[]} selected_filters + * @returns string + */ +function make_modal_body_filters(filters, selected_filters = []) { + let str = ``; + + filters.forEach((val) => { + if (selected_filters.includes(val)) { + str += `${val}`; + } else { + str += `${val}`; + } + }); + + let filter_html = ` +
+ Filters: + ${str} +
+ `; + + return filter_html; +} + +/** + * Make the result component given a minisearch result data object and the value of the search input as queryString. + * To view the result object structure, refer: https://lucaong.github.io/minisearch/modules/_minisearch_.html#searchresult + * + * @param {object} result + * @param {string} querystring + * @returns string + */ +function make_search_result(result, querystring) { + let search_divider = `
`; + let display_link = + result.location.slice(Math.max(0), Math.min(50, result.location.length)) + + (result.location.length > 30 ? "..." : ""); // To cut-off the link because it messes with the overflow of the whole div + + if (result.page !== "") { + display_link += ` (${result.page})`; + } + + let textindex = new RegExp(`\\b${querystring}\\b`, "i").exec(result.text); + let text = + textindex !== null + ? result.text.slice( + Math.max(textindex.index - 100, 0), + Math.min( + textindex.index + querystring.length + 100, + result.text.length + ) + ) + : ""; // cut-off text before and after from the match + + let display_result = text.length + ? "..." + + text.replace( + new RegExp(`\\b${querystring}\\b`, "i"), // For first occurrence + '$&' + ) + + "..." + : ""; // highlights the match + + let in_code = false; + if (!["page", "section"].includes(result.category.toLowerCase())) { + in_code = true; + } + + // We encode the full url to escape some special characters which can lead to broken links + let result_div = ` + +
+
${result.title}
+
${result.category}
+
+

+ ${display_result} +

+
+ ${display_link} +
+ + ${search_divider} + `; + + return result_div; +} + +/** + * Get selected filters, remake the filter html and lastly update the search modal + */ +function get_filters() { + let ele = $(".search-filters .search-filter-selected").get(); + filter_results = ele.map((x) => $(x).text().toLowerCase()); + modal_filters = make_modal_body_filters(filters, filter_results); + update_search(filter_results); +} + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Modal settings dialog +$(document).ready(function () { + var settings = $("#documenter-settings"); + $("#documenter-settings-button").click(function () { + settings.toggleClass("is-active"); + }); + // Close the dialog if X is clicked + $("#documenter-settings button.delete").click(function () { + settings.removeClass("is-active"); + }); + // Close dialog if ESC is pressed + $(document).keyup(function (e) { + if (e.keyCode == 27) settings.removeClass("is-active"); + }); +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +let search_modal_header = ` +
+
+

+ + + + +

+
+
+ +
+
+`; + +let initial_search_body = ` +
Type something to get started!
+`; + +let search_modal_footer = ` +
+ + Ctrl + + / to search + + esc to close +
+`; + +$(document.body).append( + ` + + ` +); + +document.querySelector(".docs-search-query").addEventListener("click", () => { + openModal(); +}); + +document.querySelector(".close-search-modal").addEventListener("click", () => { + closeModal(); +}); + +$(document).on("click", ".search-result-link", function () { + closeModal(); +}); + +document.addEventListener("keydown", (event) => { + if ((event.ctrlKey || event.metaKey) && event.key === "/") { + openModal(); + } else if (event.key === "Escape") { + closeModal(); + } + + return false; +}); + +// Functions to open and close a modal +function openModal() { + let searchModal = document.querySelector("#search-modal"); + + searchModal.classList.add("is-active"); + document.querySelector(".documenter-search-input").focus(); +} + +function closeModal() { + let searchModal = document.querySelector("#search-modal"); + let initial_search_body = ` +
Type something to get started!
+ `; + + searchModal.classList.remove("is-active"); + document.querySelector(".documenter-search-input").blur(); + + if (!$(".search-modal-card-body").hasClass("is-justify-content-center")) { + $(".search-modal-card-body").addClass("is-justify-content-center"); + } + + $(".documenter-search-input").val(""); + $(".search-modal-card-body").html(initial_search_body); +} + +document + .querySelector("#search-modal .modal-background") + .addEventListener("click", () => { + closeModal(); + }); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Manages the showing and hiding of the sidebar. +$(document).ready(function () { + var sidebar = $("#documenter > .docs-sidebar"); + var sidebar_button = $("#documenter-sidebar-button"); + sidebar_button.click(function (ev) { + ev.preventDefault(); + sidebar.toggleClass("visible"); + if (sidebar.hasClass("visible")) { + // Makes sure that the current menu item is visible in the sidebar. + $("#documenter .docs-menu a.is-active").focus(); + } + }); + $("#documenter > .docs-main").bind("click", function (ev) { + if ($(ev.target).is(sidebar_button)) { + return; + } + if (sidebar.hasClass("visible")) { + sidebar.removeClass("visible"); + } + }); +}); + +// Resizes the package name / sitename in the sidebar if it is too wide. +// Inspired by: https://github.com/davatron5000/FitText.js +$(document).ready(function () { + e = $("#documenter .docs-autofit"); + function resize() { + var L = parseInt(e.css("max-width"), 10); + var L0 = e.width(); + if (L0 > L) { + var h0 = parseInt(e.css("font-size"), 10); + e.css("font-size", (L * h0) / L0); + // TODO: make sure it survives resizes? + } + } + // call once and then register events + resize(); + $(window).resize(resize); + $(window).on("orientationchange", resize); +}); + +// Scroll the navigation bar to the currently selected menu item +$(document).ready(function () { + var sidebar = $("#documenter .docs-menu").get(0); + var active = $("#documenter .docs-menu .is-active").get(0); + if (typeof active !== "undefined") { + sidebar.scrollTop = active.offsetTop - sidebar.offsetTop - 15; + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// Theme picker setup +$(document).ready(function () { + // onchange callback + $("#documenter-themepicker").change(function themepick_callback(ev) { + var themename = $("#documenter-themepicker option:selected").attr("value"); + if (themename === "auto") { + // set_theme(window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light'); + window.localStorage.removeItem("documenter-theme"); + } else { + // set_theme(themename); + window.localStorage.setItem("documenter-theme", themename); + } + // We re-use the global function from themeswap.js to actually do the swapping. + set_theme_from_local_storage(); + }); + + // Make sure that the themepicker displays the correct theme when the theme is retrieved + // from localStorage + if (typeof window.localStorage !== "undefined") { + var theme = window.localStorage.getItem("documenter-theme"); + if (theme !== null) { + $("#documenter-themepicker option").each(function (i, e) { + e.selected = e.value === theme; + }); + } + } +}); + +}) +//////////////////////////////////////////////////////////////////////////////// +require(['jquery'], function($) { + +// update the version selector with info from the siteinfo.js and ../versions.js files +$(document).ready(function () { + // If the version selector is disabled with DOCUMENTER_VERSION_SELECTOR_DISABLED in the + // siteinfo.js file, we just return immediately and not display the version selector. + if ( + typeof DOCUMENTER_VERSION_SELECTOR_DISABLED === "boolean" && + DOCUMENTER_VERSION_SELECTOR_DISABLED + ) { + return; + } + + var version_selector = $("#documenter .docs-version-selector"); + var version_selector_select = $("#documenter .docs-version-selector select"); + + version_selector_select.change(function (x) { + target_href = version_selector_select + .children("option:selected") + .get(0).value; + window.location.href = target_href; + }); + + // add the current version to the selector based on siteinfo.js, but only if the selector is empty + if ( + typeof DOCUMENTER_CURRENT_VERSION !== "undefined" && + $("#version-selector > option").length == 0 + ) { + var option = $( + "" + ); + version_selector_select.append(option); + } + + if (typeof DOC_VERSIONS !== "undefined") { + var existing_versions = version_selector_select.children("option"); + var existing_versions_texts = existing_versions.map(function (i, x) { + return x.text; + }); + DOC_VERSIONS.forEach(function (each) { + var version_url = documenterBaseURL + "/../" + each + "/"; + var existing_id = $.inArray(each, existing_versions_texts); + // if not already in the version selector, add it as a new option, + // otherwise update the old option with the URL and enable it + if (existing_id == -1) { + var option = $( + "" + ); + version_selector_select.append(option); + } else { + var option = existing_versions[existing_id]; + option.value = version_url; + option.disabled = false; + } + }); + } + + // only show the version selector if the selector has been populated + if (version_selector_select.children("option").length > 0) { + version_selector.toggleClass("visible"); + } +}); + +}) diff --git a/v3.13.3/assets/img/bp-message.svg b/v3.13.3/assets/img/bp-message.svg new file mode 100644 index 000000000..259b70803 --- /dev/null +++ b/v3.13.3/assets/img/bp-message.svg @@ -0,0 +1 @@ + \ No newline at end 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.pagination-previous:focus,html.theme--documenter-dark .pagination-next:focus,html.theme--documenter-dark .pagination-link:focus,html.theme--documenter-dark .pagination-ellipsis:focus,html.theme--documenter-dark .file-cta:focus,html.theme--documenter-dark .file-name:focus,html.theme--documenter-dark .select select:focus,html.theme--documenter-dark .textarea:focus,html.theme--documenter-dark .input:focus,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:focus,html.theme--documenter-dark .button:focus,html.theme--documenter-dark .is-focused.pagination-previous,html.theme--documenter-dark .is-focused.pagination-next,html.theme--documenter-dark .is-focused.pagination-link,html.theme--documenter-dark .is-focused.pagination-ellipsis,html.theme--documenter-dark .is-focused.file-cta,html.theme--documenter-dark .is-focused.file-name,html.theme--documenter-dark .select select.is-focused,html.theme--documenter-dark .is-focused.textarea,html.theme--documenter-dark .is-focused.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-focused,html.theme--documenter-dark .is-focused.button,html.theme--documenter-dark .pagination-previous:active,html.theme--documenter-dark .pagination-next:active,html.theme--documenter-dark .pagination-link:active,html.theme--documenter-dark .pagination-ellipsis:active,html.theme--documenter-dark .file-cta:active,html.theme--documenter-dark .file-name:active,html.theme--documenter-dark .select select:active,html.theme--documenter-dark .textarea:active,html.theme--documenter-dark .input:active,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input:active,html.theme--documenter-dark .button:active,html.theme--documenter-dark .is-active.pagination-previous,html.theme--documenter-dark .is-active.pagination-next,html.theme--documenter-dark .is-active.pagination-link,html.theme--documenter-dark .is-active.pagination-ellipsis,html.theme--documenter-dark .is-active.file-cta,html.theme--documenter-dark .is-active.file-name,html.theme--documenter-dark .select select.is-active,html.theme--documenter-dark .is-active.textarea,html.theme--documenter-dark .is-active.input,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-active,html.theme--documenter-dark .is-active.button{outline:none}html.theme--documenter-dark .pagination-previous[disabled],html.theme--documenter-dark .pagination-next[disabled],html.theme--documenter-dark .pagination-link[disabled],html.theme--documenter-dark .pagination-ellipsis[disabled],html.theme--documenter-dark .file-cta[disabled],html.theme--documenter-dark .file-name[disabled],html.theme--documenter-dark .select select[disabled],html.theme--documenter-dark .textarea[disabled],html.theme--documenter-dark .input[disabled],html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input[disabled],html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .pagination-previous,html.theme--documenter-dark fieldset[disabled] .pagination-previous,fieldset[disabled] html.theme--documenter-dark .pagination-next,html.theme--documenter-dark fieldset[disabled] .pagination-next,fieldset[disabled] html.theme--documenter-dark .pagination-link,html.theme--documenter-dark fieldset[disabled] .pagination-link,fieldset[disabled] html.theme--documenter-dark .pagination-ellipsis,html.theme--documenter-dark fieldset[disabled] .pagination-ellipsis,fieldset[disabled] html.theme--documenter-dark .file-cta,html.theme--documenter-dark fieldset[disabled] .file-cta,fieldset[disabled] html.theme--documenter-dark .file-name,html.theme--documenter-dark fieldset[disabled] .file-name,fieldset[disabled] html.theme--documenter-dark .select select,fieldset[disabled] html.theme--documenter-dark .textarea,fieldset[disabled] html.theme--documenter-dark .input,fieldset[disabled] html.theme--documenter-dark #documenter .docs-sidebar 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.subtitle:not(:last-child),html.theme--documenter-dark .table-container:not(:last-child),html.theme--documenter-dark .table:not(:last-child),html.theme--documenter-dark .progress:not(:last-child),html.theme--documenter-dark .notification:not(:last-child),html.theme--documenter-dark .content:not(:last-child),html.theme--documenter-dark .box:not(:last-child){margin-bottom:1.5rem}html.theme--documenter-dark .modal-close,html.theme--documenter-dark .delete{-webkit-touch-callout:none;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;-moz-appearance:none;-webkit-appearance:none;background-color:rgba(10,10,10,0.2);border:none;border-radius:9999px;cursor:pointer;pointer-events:auto;display:inline-block;flex-grow:0;flex-shrink:0;font-size:0;height:20px;max-height:20px;max-width:20px;min-height:20px;min-width:20px;outline:none;position:relative;vertical-align:top;width:20px}html.theme--documenter-dark .modal-close::before,html.theme--documenter-dark 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kbd.button.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading:focus::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-dark.is-outlined.is-loading.is-focused::after,html.theme--documenter-dark .content kbd.button.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-dark.is-outlined[disabled],html.theme--documenter-dark .content kbd.button.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-dark.is-outlined,fieldset[disabled] html.theme--documenter-dark .content kbd.button.is-outlined{background-color:transparent;border-color:#282f2f;box-shadow:none;color:#282f2f}html.theme--documenter-dark .button.is-dark.is-inverted.is-outlined,html.theme--documenter-dark .content 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.button.is-danger:hover,html.theme--documenter-dark .button.is-danger.is-hovered{background-color:#92190c;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:focus,html.theme--documenter-dark .button.is-danger.is-focused{border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger:focus:not(:active),html.theme--documenter-dark .button.is-danger.is-focused:not(:active){box-shadow:0 0 0 0.125em rgba(158,27,13,0.25)}html.theme--documenter-dark .button.is-danger:active,html.theme--documenter-dark .button.is-danger.is-active{background-color:#86170b;border-color:transparent;color:#fff}html.theme--documenter-dark .button.is-danger[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger{background-color:#9e1b0d;border-color:#9e1b0d;box-shadow:none}html.theme--documenter-dark .button.is-danger.is-inverted{background-color:#fff;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted:hover,html.theme--documenter-dark .button.is-danger.is-inverted.is-hovered{background-color:#f2f2f2}html.theme--documenter-dark .button.is-danger.is-inverted[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-inverted{background-color:#fff;border-color:transparent;box-shadow:none;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-loading::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-danger.is-outlined{background-color:transparent;border-color:#9e1b0d;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-outlined:hover,html.theme--documenter-dark .button.is-danger.is-outlined.is-hovered,html.theme--documenter-dark .button.is-danger.is-outlined:focus,html.theme--documenter-dark .button.is-danger.is-outlined.is-focused{background-color:#9e1b0d;border-color:#9e1b0d;color:#fff}html.theme--documenter-dark .button.is-danger.is-outlined.is-loading::after{border-color:transparent transparent #9e1b0d #9e1b0d !important}html.theme--documenter-dark .button.is-danger.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-danger.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #fff #fff !important}html.theme--documenter-dark .button.is-danger.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-outlined{background-color:transparent;border-color:#9e1b0d;box-shadow:none;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;color:#fff}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined:hover,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-hovered,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined:focus,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-focused{background-color:#fff;color:#9e1b0d}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:hover::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-hovered::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading:focus::after,html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined.is-loading.is-focused::after{border-color:transparent transparent #9e1b0d #9e1b0d !important}html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined[disabled],fieldset[disabled] html.theme--documenter-dark .button.is-danger.is-inverted.is-outlined{background-color:transparent;border-color:#fff;box-shadow:none;color:#fff}html.theme--documenter-dark .button.is-danger.is-light{background-color:#fdeeec;color:#ec311d}html.theme--documenter-dark .button.is-danger.is-light:hover,html.theme--documenter-dark .button.is-danger.is-light.is-hovered{background-color:#fce3e0;border-color:transparent;color:#ec311d}html.theme--documenter-dark .button.is-danger.is-light:active,html.theme--documenter-dark .button.is-danger.is-light.is-active{background-color:#fcd8d5;border-color:transparent;color:#ec311d}html.theme--documenter-dark .button.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{font-size:.75rem}html.theme--documenter-dark .button.is-small:not(.is-rounded),html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button:not(.is-rounded){border-radius:3px}html.theme--documenter-dark .button.is-normal{font-size:1rem}html.theme--documenter-dark .button.is-medium{font-size:1.25rem}html.theme--documenter-dark .button.is-large{font-size:1.5rem}html.theme--documenter-dark .button[disabled],fieldset[disabled] html.theme--documenter-dark .button{background-color:#8c9b9d;border-color:#5e6d6f;box-shadow:none;opacity:.5}html.theme--documenter-dark .button.is-fullwidth{display:flex;width:100%}html.theme--documenter-dark .button.is-loading{color:transparent !important;pointer-events:none}html.theme--documenter-dark .button.is-loading::after{position:absolute;left:calc(50% - (1em * 0.5));top:calc(50% - (1em * 0.5));position:absolute !important}html.theme--documenter-dark .button.is-static{background-color:#282f2f;border-color:#5e6d6f;color:#dbdee0;box-shadow:none;pointer-events:none}html.theme--documenter-dark .button.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.button{border-radius:9999px;padding-left:calc(1em + 0.25em);padding-right:calc(1em + 0.25em)}html.theme--documenter-dark .buttons{align-items:center;display:flex;flex-wrap:wrap;justify-content:flex-start}html.theme--documenter-dark .buttons .button{margin-bottom:0.5rem}html.theme--documenter-dark .buttons .button:not(:last-child):not(.is-fullwidth){margin-right:.5rem}html.theme--documenter-dark .buttons:last-child{margin-bottom:-0.5rem}html.theme--documenter-dark .buttons:not(:last-child){margin-bottom:1rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large){font-size:.75rem}html.theme--documenter-dark .buttons.are-small .button:not(.is-normal):not(.is-medium):not(.is-large):not(.is-rounded){border-radius:3px}html.theme--documenter-dark .buttons.are-medium .button:not(.is-small):not(.is-normal):not(.is-large){font-size:1.25rem}html.theme--documenter-dark .buttons.are-large .button:not(.is-small):not(.is-normal):not(.is-medium){font-size:1.5rem}html.theme--documenter-dark .buttons.has-addons .button:not(:first-child){border-bottom-left-radius:0;border-top-left-radius:0}html.theme--documenter-dark .buttons.has-addons .button:not(:last-child){border-bottom-right-radius:0;border-top-right-radius:0;margin-right:-1px}html.theme--documenter-dark .buttons.has-addons .button:last-child{margin-right:0}html.theme--documenter-dark .buttons.has-addons .button:hover,html.theme--documenter-dark .buttons.has-addons .button.is-hovered{z-index:2}html.theme--documenter-dark .buttons.has-addons .button:focus,html.theme--documenter-dark .buttons.has-addons .button.is-focused,html.theme--documenter-dark .buttons.has-addons .button:active,html.theme--documenter-dark .buttons.has-addons .button.is-active,html.theme--documenter-dark .buttons.has-addons .button.is-selected{z-index:3}html.theme--documenter-dark .buttons.has-addons .button:focus:hover,html.theme--documenter-dark .buttons.has-addons .button.is-focused:hover,html.theme--documenter-dark .buttons.has-addons .button:active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-active:hover,html.theme--documenter-dark .buttons.has-addons .button.is-selected:hover{z-index:4}html.theme--documenter-dark .buttons.has-addons .button.is-expanded{flex-grow:1;flex-shrink:1}html.theme--documenter-dark .buttons.is-centered{justify-content:center}html.theme--documenter-dark .buttons.is-centered:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}html.theme--documenter-dark .buttons.is-right{justify-content:flex-end}html.theme--documenter-dark .buttons.is-right:not(.has-addons) .button:not(.is-fullwidth){margin-left:0.25rem;margin-right:0.25rem}@media screen and (max-width: 768px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.5625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1rem}}@media screen and (min-width: 769px) and (max-width: 1055px){html.theme--documenter-dark .button.is-responsive.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.is-responsive{font-size:.65625rem}html.theme--documenter-dark .button.is-responsive,html.theme--documenter-dark .button.is-responsive.is-normal{font-size:.75rem}html.theme--documenter-dark .button.is-responsive.is-medium{font-size:1rem}html.theme--documenter-dark .button.is-responsive.is-large{font-size:1.25rem}}html.theme--documenter-dark .container{flex-grow:1;margin:0 auto;position:relative;width:auto}html.theme--documenter-dark .container.is-fluid{max-width:none !important;padding-left:32px;padding-right:32px;width:100%}@media screen and (min-width: 1056px){html.theme--documenter-dark .container{max-width:992px}}@media screen and (max-width: 1215px){html.theme--documenter-dark .container.is-widescreen:not(.is-max-desktop){max-width:1152px}}@media screen and (max-width: 1407px){html.theme--documenter-dark .container.is-fullhd:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}@media screen and (min-width: 1216px){html.theme--documenter-dark .container:not(.is-max-desktop){max-width:1152px}}@media screen and (min-width: 1408px){html.theme--documenter-dark .container:not(.is-max-desktop):not(.is-max-widescreen){max-width:1344px}}html.theme--documenter-dark .content li+li{margin-top:0.25em}html.theme--documenter-dark .content p:not(:last-child),html.theme--documenter-dark .content dl:not(:last-child),html.theme--documenter-dark .content ol:not(:last-child),html.theme--documenter-dark .content ul:not(:last-child),html.theme--documenter-dark .content blockquote:not(:last-child),html.theme--documenter-dark .content pre:not(:last-child),html.theme--documenter-dark .content table:not(:last-child){margin-bottom:1em}html.theme--documenter-dark .content h1,html.theme--documenter-dark .content h2,html.theme--documenter-dark .content h3,html.theme--documenter-dark .content h4,html.theme--documenter-dark .content h5,html.theme--documenter-dark .content h6{color:#f2f2f2;font-weight:600;line-height:1.125}html.theme--documenter-dark .content h1{font-size:2em;margin-bottom:0.5em}html.theme--documenter-dark .content h1:not(:first-child){margin-top:1em}html.theme--documenter-dark .content h2{font-size:1.75em;margin-bottom:0.5714em}html.theme--documenter-dark .content h2:not(:first-child){margin-top:1.1428em}html.theme--documenter-dark .content h3{font-size:1.5em;margin-bottom:0.6666em}html.theme--documenter-dark .content h3:not(:first-child){margin-top:1.3333em}html.theme--documenter-dark .content h4{font-size:1.25em;margin-bottom:0.8em}html.theme--documenter-dark .content h5{font-size:1.125em;margin-bottom:0.8888em}html.theme--documenter-dark .content h6{font-size:1em;margin-bottom:1em}html.theme--documenter-dark .content blockquote{background-color:#282f2f;border-left:5px solid #5e6d6f;padding:1.25em 1.5em}html.theme--documenter-dark .content ol{list-style-position:outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ol:not([type]){list-style-type:decimal}html.theme--documenter-dark .content ol.is-lower-alpha:not([type]){list-style-type:lower-alpha}html.theme--documenter-dark .content ol.is-lower-roman:not([type]){list-style-type:lower-roman}html.theme--documenter-dark .content ol.is-upper-alpha:not([type]){list-style-type:upper-alpha}html.theme--documenter-dark .content ol.is-upper-roman:not([type]){list-style-type:upper-roman}html.theme--documenter-dark .content ul{list-style:disc outside;margin-left:2em;margin-top:1em}html.theme--documenter-dark .content ul ul{list-style-type:circle;margin-top:0.5em}html.theme--documenter-dark .content ul ul ul{list-style-type:square}html.theme--documenter-dark .content dd{margin-left:2em}html.theme--documenter-dark .content figure{margin-left:2em;margin-right:2em;text-align:center}html.theme--documenter-dark .content figure:not(:first-child){margin-top:2em}html.theme--documenter-dark .content figure:not(:last-child){margin-bottom:2em}html.theme--documenter-dark .content figure img{display:inline-block}html.theme--documenter-dark .content figure figcaption{font-style:italic}html.theme--documenter-dark .content pre{-webkit-overflow-scrolling:touch;overflow-x:auto;padding:0;white-space:pre;word-wrap:normal}html.theme--documenter-dark .content sup,html.theme--documenter-dark .content sub{font-size:75%}html.theme--documenter-dark .content table{width:100%}html.theme--documenter-dark .content table td,html.theme--documenter-dark .content table th{border:1px solid #5e6d6f;border-width:0 0 1px;padding:0.5em 0.75em;vertical-align:top}html.theme--documenter-dark .content table th{color:#f2f2f2}html.theme--documenter-dark .content table th:not([align]){text-align:inherit}html.theme--documenter-dark .content table thead td,html.theme--documenter-dark .content table thead th{border-width:0 0 2px;color:#f2f2f2}html.theme--documenter-dark .content table tfoot td,html.theme--documenter-dark .content table tfoot th{border-width:2px 0 0;color:#f2f2f2}html.theme--documenter-dark .content table tbody tr:last-child td,html.theme--documenter-dark .content table tbody tr:last-child th{border-bottom-width:0}html.theme--documenter-dark .content .tabs li+li{margin-top:0}html.theme--documenter-dark .content.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.content{font-size:.75rem}html.theme--documenter-dark .content.is-normal{font-size:1rem}html.theme--documenter-dark .content.is-medium{font-size:1.25rem}html.theme--documenter-dark .content.is-large{font-size:1.5rem}html.theme--documenter-dark .icon{align-items:center;display:inline-flex;justify-content:center;height:1.5rem;width:1.5rem}html.theme--documenter-dark .icon.is-small,html.theme--documenter-dark #documenter .docs-sidebar form.docs-search>input.icon{height:1rem;width:1rem}html.theme--documenter-dark .icon.is-medium{height:2rem;width:2rem}html.theme--documenter-dark .icon.is-large{height:3rem;width:3rem}html.theme--documenter-dark .icon-text{align-items:flex-start;color:inherit;display:inline-flex;flex-wrap:wrap;line-height:1.5rem;vertical-align:top}html.theme--documenter-dark .icon-text .icon{flex-grow:0;flex-shrink:0}html.theme--documenter-dark .icon-text .icon:not(:last-child){margin-right:.25em}html.theme--documenter-dark .icon-text .icon:not(:first-child){margin-left:.25em}html.theme--documenter-dark div.icon-text{display:flex}html.theme--documenter-dark .image,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img{display:block;position:relative}html.theme--documenter-dark .image img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img{display:block;height:auto;width:100%}html.theme--documenter-dark .image img.is-rounded,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img img.is-rounded{border-radius:9999px}html.theme--documenter-dark .image.is-fullwidth,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-fullwidth{width:100%}html.theme--documenter-dark .image.is-square img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square img,html.theme--documenter-dark .image.is-square .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-square .has-ratio,html.theme--documenter-dark .image.is-1by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 img,html.theme--documenter-dark .image.is-1by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-1by1 .has-ratio,html.theme--documenter-dark .image.is-5by4 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 img,html.theme--documenter-dark .image.is-5by4 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by4 .has-ratio,html.theme--documenter-dark .image.is-4by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 img,html.theme--documenter-dark .image.is-4by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by3 .has-ratio,html.theme--documenter-dark .image.is-3by2 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 img,html.theme--documenter-dark .image.is-3by2 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by2 .has-ratio,html.theme--documenter-dark .image.is-5by3 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 img,html.theme--documenter-dark .image.is-5by3 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-5by3 .has-ratio,html.theme--documenter-dark .image.is-16by9 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 img,html.theme--documenter-dark .image.is-16by9 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-16by9 .has-ratio,html.theme--documenter-dark .image.is-2by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1 img,html.theme--documenter-dark .image.is-2by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-2by1 .has-ratio,html.theme--documenter-dark .image.is-3by1 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1 img,html.theme--documenter-dark .image.is-3by1 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-3by1 .has-ratio,html.theme--documenter-dark .image.is-4by5 img,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5 img,html.theme--documenter-dark .image.is-4by5 .has-ratio,html.theme--documenter-dark #documenter .docs-sidebar .docs-logo>img.is-4by5 .has-ratio,html.theme--documenter-dark .image.is-3by4 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Click here to go to the documentation for the latest stable release.'; + div.appendChild(closer); + document.body.appendChild(div); +} + +if (document.readyState === "loading") { + document.addEventListener("DOMContentLoaded", maybeAddWarning); +} else { + maybeAddWarning(); +} diff --git a/v3.13.3/custom/custom-addons/index.html b/v3.13.3/custom/custom-addons/index.html new file mode 100644 index 000000000..17346602f --- /dev/null +++ b/v3.13.3/custom/custom-addons/index.html @@ -0,0 +1,24 @@ + +Custom addons · ReactiveMP.jl
GitHub

Custom Addons

Standard message passing schemes only pass along distributions to other nodes. However, for more advanced usage, there might be a need for passing along additional information in messages and/or marginals. One can for example think of passing along the scaling of the distribution or some information that specifies how the message or marginal was computed, i.e. which messages were used for its computation and which node was preceding it. Another use cases is saving extra debugging information inside messages themselves, e.g. what arguments have been used to compute a message.

Addons provide a solution here. Basically, addons are structures that contain extra information that are passed along the graph with messages and marginals in a tuple. These addons can be extracted using the getaddons(message/marginal) function. Its usage and operations can differ significantly for each application, yet below gives a concise overview on how to implement them on your own.

Example

Suppose that we wish to create an addon that counts the number of computations that preceded some message or marginal. This addon can be created by adding the file src/addons/count.jl and by including it in the ReactiveMP.jl file.

Step 1: Creating the addon structure

Let's start by defining our new addon structure. This might seem daunting, but basically only requires us to specify the information that we would like to collect. Just make sure that it is specified as a subtype of AbstractAddon. In our example this becomes:

struct AddonCount{T} <: AbstractAddon
+    count :: T
+end

You can add additional fields or functions for improved handling, such as get_count() or show() functions.

Step 2: Compute addon value after computing a message

As a second step we need to specify how the addon behaves when a new message is computed in a factor node. For this purpose we need to implement a specialized version of the message_mapping_addon() function. This function accepts the mapping variables of the factor node and updates the addons by extending the tuple.

In our example we could write

# This specification assumes that the default value for addon is `AddonCount(nothing)`
+function message_mapping_addon(::AddonCount{Nothing}, mapping, messages, marginals, result, addons)
+
+    # get number of operations of messages
+    message_count = 0
+    for message in messages
+        message_count += getcount(message)
+    end
+
+    # get number of operations of marginals
+    marginal_count = 0
+    for marginal in marginals
+        marginal_count += getcount(marginal)
+    end
+
+    # extend addons with AddonCount() structure
+    return AddonCount(message_count + marginal_count + 1)
+end
+

Step 3: Computing products

The goal is to update the AddonCount structure when we multiply 2 messages. As a result, we need to write a function that allows us to define this behaviour. This function is called multiply_addons and accepts 5 arguments. In our example this becomes

function multiply_addons(left_addon::AddonCount, right_addon::AddonCount, new_dist, left_dist, right_dist)
+    return AddonCount(left_addon.count + right_addon.count + 1)
+end

here we add the number of operations from the addons that are being multiplied and we add one (for the current operation). we are aware that this is likely not valid for iterative message passing schemes, but it still serves as a nice example. the left_addon and right_addon argument specify the addoncount objects that are being multiplied. corresponding to these addons, there are the distributions left_dist and right_dist, which might contain information for computing the product. the new distribution new_dist ∝ left_dist * right_dist is also passed along for potentially reusing the result of earlier computations.

More information

For more advanced information check the implementation of the log-scale or memory addons.

diff --git a/v3.13.3/custom/custom-functional-form/index.html b/v3.13.3/custom/custom-functional-form/index.html new file mode 100644 index 000000000..3ff7dafc9 --- /dev/null +++ b/v3.13.3/custom/custom-functional-form/index.html @@ -0,0 +1,21 @@ + +Custom functional form · ReactiveMP.jl
GitHub

Custom Functional Form Specification

In a nutshell, functional form constraints defines a function that approximates the product of colliding messages and computes posterior marginal that can be used later on during the inference procedure. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. For example, if we refer to our CustomFunctionalForm as to f we can see the whole functional form constraints pipeline as:

\[q(x) = f\left(\frac{\overrightarrow{\mu}(x)\overleftarrow{\mu}(x)}{\int \overrightarrow{\mu}(x)\overleftarrow{\mu}(x) \mathrm{d}x}\right)\]

Interface

ReactiveMP.jl, however, uses some extra utility functions to define functional form constraint behaviour. Here we briefly describe all utility function. If you are only interested in the concrete example, you may directly head to the Custom Functional Form example at the end of this section.

Abstract super type

ReactiveMP.AbstractFormConstraint — Type
AbstractFormConstraint

Every functional form constraint is a subtype of AbstractFormConstraint abstract type.

Note: this is not strictly necessary, but it makes automatic dispatch easier and compatible with the CompositeFormConstraint.

See also: CompositeFormConstraint

source
ReactiveMP.CompositeFormConstraint — Type
CompositeFormConstraint

Creates a composite form constraint that applies form constraints in order. The composed form constraints must be compatible and have the exact same form_check_strategy. Any functional form constraint that defines is_point_mass_form_constraint() = true may be used only as the last element of the composition.

source

Form check strategy

Every custom functional form must implement a new method for the default_form_check_strategy function that returns either FormConstraintCheckEach or FormConstraintCheckLast.

  • FormConstraintCheckLast: q(x) = f(μ1(x) * μ2(x) * μ3(x))
  • FormConstraintCheckEach: q(x) = f(f(μ1(x) * μ2(x)) * μ3(x))
ReactiveMP.FormConstraintCheckEach — Type
FormConstraintCheckEach

This form constraint check strategy checks functional form of the messages product after each product in an equality chain. Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed after each subsequent prod function.

See also: FormConstraintCheckLast, default_form_check_strategy, constrain_form

source
ReactiveMP.FormConstraintCheckLast — Type
FormConstraintCheckEach

This form constraint check strategy checks functional form of the last messages product in the equality chain. Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed only once after all subsequenct prod functions have been executed.

See also: FormConstraintCheckLast, default_form_check_strategy, constrain_form

source

Prod constraint

Every custom functional form must implement a new method for the default_prod_constraint function that returns a proper prod_constraint object.

ReactiveMP.default_prod_constraint — Function
default_prod_constraint(form_constraint)

Returns a default prod constraint needed to apply a given form_constraint. For most form constraints this function returns ProdGeneric.

source

Constrain form, a.k.a f

The main function that a custom functional form must implement, which we referred to as f in the beginning of this section, is the constrain_form function.

Is point mass form constraint (optional)

Every custom functional form may implement a new method for the is_point_mass_form_constraint function that returns either true or false. This is an utility function that simplifes computation of the Bethe Free Energy and is not strictly necessary.

Compatibility with @constraints macro (optional)

To make custom functional form constraint compatible with the @constraints macro, it must implement a new method for the make_form_constraint function.

ReactiveMP.make_form_constraint — Function
make_form_constraint(::Type, args...; kwargs...)

Creates form constraint object based on passed type with given args and kwargs. Used to simplify form constraint specification.

As an example:

make_form_constraint(PointMass)

creates an instance of PointMassFormConstraint and 

make_form_constraint(SampleList, 5000, LeftProposal())

should create an instance of SampleListFormConstraint.

See also: AbstractFormConstraint

source

Custom Functional Form Example

In this demo we show how to build a custom functional form constraint that is compatible with the ReactiveMP.jl inference backend. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. We show a relatively simple use-case, which might not be very useful in practice, but serves as a simple step-by-step guide. Assume that we want a specific posterior marginal of some random variable in our model to have a specific Gaussian parametrisation, for example mean-precision. We can use built-in NormalMeanPrecision distribution, but we still need to define our custom functional form constraint:

using ReactiveMP, BayesBase
+
+# First we define our functional form structure with no fields
+struct MeanPrecisionFormConstraint <: AbstractFormConstraint end

Next we define the behaviour of our functional form constraint:

ReactiveMP.is_point_mass_form_constraint(::MeanPrecisionFormConstraint) = false
+ReactiveMP.default_form_check_strategy(::MeanPrecisionFormConstraint)   = FormConstraintCheckLast()
+ReactiveMP.default_prod_constraint(::MeanPrecisionFormConstraint)       = GenericProd()
+
+function ReactiveMP.constrain_form(::MeanPrecisionFormConstraint, distribution)
+    # This is quite a naive assumption, that a given `dsitribution` object has `mean` and `precision` defined
+    # However this quantities might be approximated with some other external method, e.g. Laplace approximation
+    m = mean(distribution)      # or approximate with some other method
+    p = precision(distribution) # or approximate with some other method
+    return NormalMeanPrecision(m, p)
+end
+
+function ReactiveMP.constrain_form(::MeanPrecisionFormConstraint, distribution::BayesBase.ProductOf)
+    # ProductOf is the special case, read about this type more in the corresponding documentation section
+    # of the `BayesBase` package
+    # ...
+end
diff --git a/v3.13.3/extra/contributing/index.html b/v3.13.3/extra/contributing/index.html new file mode 100644 index 000000000..c5646407a --- /dev/null +++ b/v3.13.3/extra/contributing/index.html @@ -0,0 +1,2 @@ + +Contributing · ReactiveMP.jl
GitHub

Contribution guidelines

We welcome all possible contributors. This page details the some of the guidelines that should be followed when contributing to this package.

Reporting bugs

We track bugs using GitHub issues. We encourage you to write complete, specific, reproducible bug reports. Mention the versions of Julia and ReactiveMP for which you observe unexpected behavior. Please provide a concise description of the problem and complement it with code snippets, test cases, screenshots, tracebacks or any other information that you consider relevant. This will help us to replicate the problem and narrow the search space for solutions.

Suggesting features

We welcome new feature proposals. However, before submitting a feature request, consider a few things:

  • Does the feature require changes in the core ReactiveMP.jl code? If it doesn't (for example, you would like to add a factor node for a particular application), you can add local extensions in your script/notebook or consider making a separate repository for your extensions.
  • If you would like to add an implementation of a feature that changes a lot in the core ReactiveMP.jl code, please open an issue on GitHub and describe your proposal first. This will allow us to discuss your proposal with you before you invest your time in implementing something that may be difficult to merge later on.

Contributing code

Installing ReactiveMP

We suggest that you use the dev command from the new Julia package manager to install ReactiveMP.jl for development purposes. To work on your fork of ReactiveMP.jl, use your fork's URL address in the dev command, for example:

] dev git@github.com:your_username/ReactiveMP.jl.git

The dev command clones ReactiveMP.jl to ~/.julia/dev/ReactiveMP. All local changes to ReactiveMP code will be reflected in imported code.

Note

It is also might be useful to install Revise.jl package as it allows you to modify code and use the changes without restarting Julia.

Committing code

We use the standard GitHub Flow workflow where all contributions are added through pull requests. In order to contribute, first fork the repository, then commit your contributions to your fork, and then create a pull request on the master branch of the ReactiveMP.jl repository.

Before opening a pull request, please make sure that all tests pass without failing! All demos (can be found in /demo/ directory) and benchmarks (can be found in /benchmark/ directory) have to run without errors as well.

Style conventions

Note

ReactiveMP.jl repository contains scripts to automatically format code according to our guidelines. Use make format command to fix code style. This command overwrites files.

We use default Julia style guide. We list here a few important points and our modifications to the Julia style guide:

  • Use 4 spaces for indentation
  • Type names use UpperCamelCase. For example: AbstractFactorNode, RandomVariable, etc..
  • Function names are lowercase with underscores, when necessary. For example: activate!, randomvar, as_variable, etc..
  • Variable names and function arguments use snake_case
  • The name of a method that modifies its argument(s) must end in !

Unit tests

We use the test-driven development (TDD) methodology for ReactiveMP.jl development. The test coverage should be as complete as possible. Please make sure that you write tests for each piece of code that you want to add.

All unit tests are located in the /test/ directory. The /test/ directory follows the structure of the /src/ directory. Each test file should have following filename format: test_*.jl. Some tests are also present in jldoctest docs annotations directly in the source code. See Julia's documentation about doctests.

The tests can be evaluated by running following command in the Julia REPL:

] test ReactiveMP

In addition tests can be evaluated by running following command in the ReactiveMP root directory:

make test

Fixes to external libraries

If a bug has been discovered in an external dependencies of the ReactiveMP.jl it is the best to open an issue directly in the dependency's github repository. You use can use the fixes.jl file for hot-fixes before a new release of the broken dependecy is available.

Makefile

ReactiveMP.jl uses Makefile for most common operations:

  • make help: Shows help snippet
  • make test: Run tests, supports extra arguments
    • make test test_args="distributions:normal_mean_variance" would run tests only from distributions/test_normal_mean_variance.jl
    • make test test_args="distributions:normal_mean_variance models:lgssm" would run tests both from distributions/test_normal_mean_variance.jl and models/test_lgssm.jl
  • make docs: Compile documentation
  • make benchmark: Run simple benchmark
  • make lint: Check codestyle
  • make format: Check and fix codestyle
diff --git a/v3.13.3/extra/extensions/index.html b/v3.13.3/extra/extensions/index.html new file mode 100644 index 000000000..bbf70e8e8 --- /dev/null +++ b/v3.13.3/extra/extensions/index.html @@ -0,0 +1,2 @@ + +Extensions and interaction with the Julia ecosystem · ReactiveMP.jl
GitHub

Extensions and interaction with the Julia ecosystem

ReactiveMP.jl exports extra functionality if other Julia packages are loaded in the same environment.

Optimisers.jl

The Optimizers.jl package defines many standard gradient-based optimisation rules, and tools for applying them to deeply nested models. The optimizers defined in the Optimziers.jl are compatible with the CVI approximation method.

DiffResults.jl (loaded automatically with the ForwardDiff.jl)

The DiffResults.jl provides the DiffResult type, which can be passed to in-place differentiation methods instead of an output buffer. If loaded in the current Julia session enables faster derivatives with the ForwardDiffGrad option in the CVI approximation method (in the Gaussian case).

diff --git a/v3.13.3/index.html b/v3.13.3/index.html new file mode 100644 index 000000000..9264094f9 --- /dev/null +++ b/v3.13.3/index.html @@ -0,0 +1,2 @@ + +Introduction · ReactiveMP.jl
GitHub

ReactiveMP.jl

Julia package for reactive message passing Bayesian inference engine on a factor graph.

Note

This package exports only an inference engine, for the full ecosystem with convenient model and constraints specification we refer user to the RxInfer.jl package and its documentation.

Examples and tutorials

Tutorials and examples are available in the RxInfer documentation.

Table of Contents

Index

diff --git a/v3.13.3/lib/algebra/common/index.html b/v3.13.3/lib/algebra/common/index.html new file mode 100644 index 000000000..654857d5b --- /dev/null +++ b/v3.13.3/lib/algebra/common/index.html @@ -0,0 +1,2 @@ + +Algebra utils · ReactiveMP.jl
GitHub

Algebra common utilities

diageye

ReactiveMP.diageye — Function
diageye(::Type{T}, n::Int)

An alias for the Matrix{T}(I, n, n). Returns a matrix of size n x n with ones (of type T) on the diagonal and zeros everywhere else.

source
diageye(n::Int)

An alias for the Matrix{Float64}(I, n, n). Returns a matrix of size n x n with ones (of type Float64) on the diagonal and zeros everywhere else.

source
diff --git a/v3.13.3/lib/helpers/index.html b/v3.13.3/lib/helpers/index.html new file mode 100644 index 000000000..6818f0f7b --- /dev/null +++ b/v3.13.3/lib/helpers/index.html @@ -0,0 +1,10 @@ + +Helper utils · ReactiveMP.jl
GitHub

Helper utilities

ReactiveMP implements various structures/functions/methods as "helper" structures that might be useful in various contexts.

SkipIndexIterator

ReactiveMP.SkipIndexIterator — Type
SkipIndexIterator

A special type of iterator that simply iterates over internal iterator, but skips index skip.

Arguments

  • iterator: internal iterator
  • skip: index to skip (integer)

See also: skipindex

source
ReactiveMP.skipindex — Function
skipindex(iterator, skip)

Creation operator for SkipIndexIterator.

julia> s = ReactiveMP.skipindex(1:3, 2)
+2-element ReactiveMP.SkipIndexIterator{Int64, UnitRange{Int64}}:
+ 1
+ 3
+
+julia> collect(s)
+2-element Vector{Int64}:
+ 1
+ 3

See also: SkipIndexIterator

source

FunctionalIndex

ReactiveMP.FunctionalIndex — Type
FunctionalIndex

A special type of an index that represents a function that can be used only in pair with a collection. An example of a FunctionalIndex can be firstindex or lastindex, but more complex use cases are possible too, e.g. firstindex + 1. Important part of the implementation is that the resulting structure is isbitstype(...) = true, that allows to store it in parametric type as valtype.

One use case for this structure is to dispatch on and to replace begin or end (or more complex use cases, e.g. begin + 1) markers in constraints specification language.

source
diff --git a/v3.13.3/lib/message/index.html b/v3.13.3/lib/message/index.html new file mode 100644 index 000000000..4dcef6bb5 --- /dev/null +++ b/v3.13.3/lib/message/index.html @@ -0,0 +1,20 @@ + +Messages · ReactiveMP.jl
GitHub

Messages implementation

In our message passing framework one of the most important concepts is the message (wow!). Messages flow along edges of a factor graph and hold information about the part of the graph that it originates from. Usually this information is in a form of a probability distribution. Two common messages are belief propagation messages and variational messages, with are computed differently as shown below.

Abstract message type

Both belief propagation and variational messages are subtypes of a AbstractMessage supertype.

Belief-Propagation (or Sum-Product) message

message Belief propagation message

Variational message

message Variational message with structured factorisation q(x, y)q(z) assumption

Message type

All messages are encoded with the type Message.

ReactiveMP.Message — Type
Message{D, A} <: AbstractMessage

Message structure encodes a Belief Propagation message, which holds some data that usually a probability distribution, but can also be an arbitrary object. Message acts as a proxy structure to data object and proxies most of the statistical functions, e.g. mean, mode, cov etc.

Arguments

  • data::D: message always holds some data object associated with it
  • is_clamped::Bool, specifies if this message is clamped
  • is_initial::Bool, specifies if this message is initial
  • addons::A, specifies the addons of the message

Example

julia> distribution = Gamma(10.0, 2.0)
+Distributions.Gamma{Float64}(α=10.0, θ=2.0)
+
+julia> message = Message(distribution, false, true, nothing)
+Message(Distributions.Gamma{Float64}(α=10.0, θ=2.0))
+
+julia> mean(message) 
+20.0
+
+julia> getdata(message)
+Distributions.Gamma{Float64}(α=10.0, θ=2.0)
+
+julia> is_clamped(message)
+false
+
+julia> is_initial(message)
+true
+

See also: AbstractMessage, ReactiveMP.materialize!

source

From an implementation point a view the Message structure does nothing but hold some data object and redirects most of the statistical related functions to that data object. However, this object is used extensively in Julia's multiple dispatch. Our implementation also uses extra is_initial and is_clamped fields to determine if product of two messages results in is_initial or is_clamped posterior marginal. The final field contains the addons. These contain additional information on top of the functional form of the distribution, such as its scaling or computation history.

distribution = NormalMeanPrecision(0.0, 1.0)
+message      = Message(distribution, false, true, nothing)
Message(ExponentialFamily.NormalMeanPrecision{Float64}(μ=0.0, w=1.0))
mean(message), precision(message)
(0.0, 1.0)
logpdf(message, 1.0)
-1.4189385332046727
is_clamped(message), is_initial(message)
(false, true)

The user should not really interact with Message structure while working with ReactiveMP unless doing some advanced inference procedures that involve prediction.

diff --git a/v3.13.3/lib/methods/index.html b/v3.13.3/lib/methods/index.html new file mode 100644 index 000000000..c663db37c --- /dev/null +++ b/v3.13.3/lib/methods/index.html @@ -0,0 +1,197 @@ + +Exported methods · ReactiveMP.jl
GitHub

Using methods from ReactiveMP

In the Julia programming language (in contrast to Python for example) the most common way of loading a module is:

using ReactiveMP

A nice explanation about how modules/packages work in Julia can be found in the official documentation.

In a nutshell, Julia automatically resolves all name collisions and there is no a lot of benefit of importing specific names, e.g.:

import ReactiveMP: mean

One of the reasons for that is that Julia uses multiple-dispatch capabilities to merge names automatically and will indicate (with a warning) if something went wrong or names have unresolvable collisions on types. As a small example of this feature consider the following small import example:

import ReactiveMP: mean as mean_from_reactivemp
+import Distributions: mean as mean_from_distributions
+import BayesBase: mean as mean_from_bayes_base
mean_from_reactivemp === mean_from_distributions
true
mean_from_reactivemp === mean_from_bayes_base
true

Even though we import mean function from different packages they actually refer to the same object. Worth noting that this is not always the case - Julia will print a warning in case it finds unresolvable conflicts and usage of such functions will be disallowed unless user import them specifically. Read more about this in the section of the Julia's documentation.

# It is easier to let Julia resolve names automatically
+# Julia will not overwrite `mean` that is coming from both packages
+using ReactiveMP, Distributions, BayesBase, ExponentialFamily
mean(Normal(0.0, 1.0)) # `Normal` is an object from `Distributions.jl`
0.0
mean(NormalMeanVariance(0.0, 1.0)) # `NormalMeanVariance` is an object from `ReactiveMP.jl`
0.0

List of available methods

Below you can find a list of exported methods from ReactiveMP.jl. All methods (even private) can be always accessed with ReactiveMP. prefix, e.g ReactiveMP.mean.

Note

Some exported names are (for legacy reasons) intended for private usage only. As a result some of these methods do not have a proper associated documentation with them. We constantly improve ReactiveMP.jl library and continue to add better documentation for many exported methods, but a small portion of these methods could be removed from this list in the future.

foreach(println, names(ReactiveMP))
@average_energy
+@call_marginalrule
+@call_rule
+@logscale
+@marginalrule
+@node
+@rule
+AND
+AR
+ARMeta
+ARsafe
+ARunsafe
+AbstractApproximationMethod
+AbstractFormConstraint
+AbstractMessage
+AbstractPipelineStage
+AbstractVariable
+Adam
+AdditiveCouplingLayer
+AddonDebug
+AddonLogScale
+AddonMemory
+AsyncPipelineStage
+Autoregressive
+AverageEnergy
+BIFM
+BIFMHelper
+BIFMMeta
+CTMeta
+CTransition
+CVI
+CompanionMatrix
+CompanionMatrixTransposed
+CompiledFlowModel
+CompositeFormConstraint
+CompositePipelineStage
+ConstVariable
+ConstraintsSpecification
+ContinuousTransition
+ContinuousTransitionMeta
+CustomProdStrategy
+DataVariable
+DefaultConstraints
+DefaultMeta
+DeltaFn
+DeltaFnNode
+DeltaMeta
+Deterministic
+DifferentialEntropy
+DiscontinuePipelineStage
+EmptyPipelineStage
+FactorBoundFreeEnergy
+FactorNodeCreationOptions
+FactorNodesCollection
+Flow
+FlowMeta
+FlowModel
+FoldLeftProdStrategy
+FoldRightProdStrategy
+FormConstraintCheckEach
+FormConstraintCheckLast
+FormConstraintCheckPickDefault
+ForwardDiffGrad
+FullFactorisation
+GCV
+GCVMetadata
+GammaMixture
+GammaMixtureNode
+GaussHermiteCubature
+GaussLaguerreQuadrature
+GaussianMixture
+GaussianMixtureNode
+HalfNormal
+IMPLY
+ImportanceSamplingApproximation
+IncludeAll
+InputLayer
+InverseWishart
+LaplaceApproximation
+Linearization
+LoggerPipelineStage
+Marginal
+Marginalisation
+MeanField
+Message
+MetaSpecification
+Mixture
+MixtureNode
+MomentMatching
+MvNormalMeanScalePrecision
+NOT
+NormalMixture
+NormalMixtureNode
+OR
+PermutationLayer
+PermutationMatrix
+PlanarFlow
+Poisson
+Probit
+ProbitMeta
+ProdCVI
+RadialFlow
+RandomVariable
+RandomVariableCreationOptions
+ReactiveMP
+SkipClamped
+SkipClampedAndInitial
+SkipInitial
+SoftDot
+StandardBasisVector
+Stochastic
+Transition
+UT
+Uninformative
+Unscented
+UnscentedTransform
+UnspecifiedConstraints
+UnspecifiedFormConstraint
+UnspecifiedMeta
+VariableBoundEntropy
+VariablesCollection
+VariationalMessage
+apply_pipeline_stage
+approximation_name
+approximation_short_name
+as_marginal
+as_message
+as_variable
+compile
+constrain_form
+constvar
+datavar
+default_form_check_strategy
+default_prod_constraint
+degree
+diageye
+dot
+factorisation
+functional_dependencies
+functionalform
+getapproximation
+getconst
+getconstant
+getdata
+getlayers
+getlogscale
+getmarginal
+getmarginals
+getmemory
+getmodel
+getnode_ids
+getnodes
+getprediction
+getpredictions
+getrandom
+getvardict
+ghcubature
+hasconstvar
+hasdatavar
+hasrandomvar
+huge
+interfaces
+is_clamped
+is_initial
+is_marginalisation
+is_moment_matching
+is_point_mass_form_constraint
+isconnected
+isdeterministic
+isstochastic
+laplace
+localmarginalnames
+localmarginals
+make_form_constraint
+make_node
+marginalrule
+metadata
+name
+nr_params
+randomvar
+rule
+score
+sdtype
+setmarginal!
+setmarginals!
+setmessage!
+setmessages!
+skipindex
+softdot
+srcubature
+tiny
+update!
diff --git a/v3.13.3/lib/nodes/flow/index.html b/v3.13.3/lib/nodes/flow/index.html new file mode 100644 index 000000000..22fe285f2 --- /dev/null +++ b/v3.13.3/lib/nodes/flow/index.html @@ -0,0 +1,9 @@ + +Flow · ReactiveMP.jl
GitHub

Flow node

See also Flow tutorial for a comprehensive guide on using flows in ReactiveMP.

ReactiveMP.PlanarFlow — Type

The PlanarFlow function is defined as

\[f({\bf{x}}) = {\bf{x}} + {\bf{u}} \tanh({\bf{w}}^\top {\bf{x}} + b)\]

with input and output dimension $D$. Here ${\bf{x}}\in \mathbb{R}^D$ represents the input of the function. Furthermore ${\bf{u}}\in \mathbb{R}^D$, ${\bf{w}}\in \mathbb{R}^D$ and $b\in\mathbb{R}$ represent the parameters of the function. The function contracts and expands the input space.

This function has been introduced in:

Rezende, Danilo, and Shakir Mohamed. "Variational inference with normalizing flows." International conference on machine learning. PMLR, 2015.

source
ReactiveMP.RadialFlow — Type

The RadialFlow function is defined as

\[f({\bf{x}}) = {\bf{x}} + \frac{\beta({bf{z}} - {\bf{z}}_0)}{\alpha + |{\bf{z}} - {\bf{z}}_0|}\]

with input and output dimension $D$. Here ${\bf{x}}\in \mathbb{R}^D$ represents the input of the function. Furthermore ${\bf{z}}_0\in \mathbb{R}^D$, $\alpha\in \mathbb{R}$ and $\beta\in\mathbb{R}$ represent the parameters of the function. The function contracts and expands the input space.

This function has been introduced in:

Rezende, Danilo, and Shakir Mohamed. "Variational inference with normalizing flows." International conference on machine learning. PMLR, 2015.

source
ReactiveMP.FlowModel — Type

The FlowModel structure is the most generic type of Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimensionality and a tuple of layers and can be constructed as FlowModel( dim, (layer1, layer2, ...) ).

Note: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.

source
ReactiveMP.CompiledFlowModel — Type

The CompiledFlowModel structure is the most generic type of compiled Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimension and a tuple of compiled layers. Do not manually create a CompiledFlowModel! Instead create a FlowModel first and compile it with compile(model::FlowModel). This will make sure that all layers/mappings are configured with the proper dimensionality and with randomly sampled parameters. Alternatively, if you would like to pass your own parameters, call compile(model::FlowModel, params::Vector).

Note: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.

source
ReactiveMP.compile — Function

compile() compiles a model by setting its parameters. It randomly sets parameter values in the layers and flows such that inference in the model can be obtained.

Input arguments

  • model::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.

Return arguments

  • ::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.
source

compile(model::FlowModel, params::Vector) lets you initialize a model model with a vector of parameters params.

Input arguments

  • model::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.
  • params::Vector - a vector of parameters with which the model should be compiled.

Return arguments

  • ::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.
source
ReactiveMP.AdditiveCouplingLayer — Type

The additive coupling layer specifies an invertible function ${\bf{y}} = g({\bf{x}})$ following the specific structure (for the mapping $g: \mathbb{R}^2 \rightarrow \mathbb{R}^2$):

\[ \begin{align} + y_1 &= x_1 \\ + y_2 &= x_2 + f(x_1) + \end{align}\]

where $f(\cdot)$ denotes an arbitrary function with mapping $f: \mathbb{R} \rightarrow \mathbb{R}$. This function can be chosen arbitrarily complex. Non-linear functions (neural networks) are often chosen to model complex relationships. From the definition of the model, invertibility can be easily achieved as

\[ \begin{align} + x_1 &= y_1 \\ + x_2 &= y_2 - f(y_1) + \end{align}\]

The current implementation only allows for the mapping $g: \mathbb{R}^2 \rightarrow \mathbb{R}^2$, although this layer can be generalized for arbitrary input dimensions.

AdditiveCouplingLayer(f <: AbstractCouplingFlow) creates the layer structure with function f.

Example

f = PlanarFlow()
+layer = AdditiveCouplingLayer(f)

This layer structure has been introduced in:

Dinh, Laurent, David Krueger, and Yoshua Bengio. "Nice: Non-linear independent components estimation." arXiv preprint arXiv:1410.8516 (2014).

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ReactiveMP.PermutationLayer — Type

The permutation layer specifies an invertible mapping ${\bf{y}} = g({\bf{x}}) = P{\bf{x}}$ where $P$ is a permutation matrix.

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ReactiveMP.FlowMeta — Type

The FlowMeta structure contains the meta data of the Flow factor node. More specifically, it contains the model of the Flow factor node. The FlowMeta structure can be constructed as FlowMeta(model). Make sure that the flow model has been compiled.

The FlowMeta structure is required for the Flow factor node and can be included with the Flow node as: y ~ Flow(x) where { meta = FlowMeta(...) }

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diff --git a/v3.13.3/lib/nodes/nodes/index.html b/v3.13.3/lib/nodes/nodes/index.html new file mode 100644 index 000000000..97043146e --- /dev/null +++ b/v3.13.3/lib/nodes/nodes/index.html @@ -0,0 +1,37 @@ + +Overview · ReactiveMP.jl
GitHub

Nodes implementation

In the message passing framework, one of the most important concepts is a factor node. A factor node represents a local function in a factorised representation of a generative model.

Note

To quickly check the list of all available factor nodes that can be used in the model specification language, call ?make_node or Base.doc(make_node).

Adding a custom node

ReactiveMP.jl exports the @node macro that allows for quick definition of a factor node with a fixed number of edges. The interface is the following:

struct MyNewCustomNode end
+
+@node MyNewCustomNode   Stochastic         [ x, y, z ]
+#     ^^^^^^^^^^^^^^^   ^^^^^^^^^^^^^      ^^^^^^^^^^^
+#     Node's tag/name   Node's type        A fixed set of edges
+#                       Another possible   The very first edge (in this example `x`) is considered
+#                       value is           to be the output of the node
+#                       `Deterministic`

This expression registers a new node that can be used with the inference engine. Note howeve, that the @node macro does not generate any message passing update rules. These must be defined using the @rule macro.

Node types

We distinguish different types of factor nodes in order to have better control over Bethe Free Energy computation. Each factor node has either the Deterministic or Stochastic functional form type.

For example the + node has the Deterministic type:

plus_node = make_node(+)
+
+println("Is `+` node deterministic: ", isdeterministic(plus_node))
+println("Is `+` node stochastic: ", isstochastic(plus_node))
Is `+` node deterministic: true
+Is `+` node stochastic: false

On the other hand, the Bernoulli node has the Stochastic type:

bernoulli_node = make_node(Bernoulli)
+
+println("Is `Bernoulli` node deterministic: ", isdeterministic(bernoulli_node))
+println("Is `Bernoulli` node stochastic: ", isstochastic(bernoulli_node))
Is `Bernoulli` node deterministic: false
+Is `Bernoulli` node stochastic: true

To get an actual instance of the type object we use sdtype function:

println("sdtype() of `+` node is ", sdtype(plus_node))
+println("sdtype() of `Bernoulli` node is ", sdtype(bernoulli_node))
sdtype() of `+` node is Deterministic()
+sdtype() of `Bernoulli` node is Stochastic()

Node functional dependencies pipeline

The generic implementation of factor nodes in ReactiveMP supports custom functional dependency pipelines. Briefly, the functional dependencies pipeline defines what dependencies are need to compute a single message. As an example, consider the belief-propagation message update equation for a factor node $f$ with three edges: $x$, $y$ and $z$:

\[\mu(x) = \int \mu(y) \mu(z) f(x, y, z) \mathrm{d}y \mathrm{d}z\]

Here we see that in the standard setting for the belief-propagation message out of edge $x$, we need only messages from the edges $y$ and $z$. In contrast, consider the variational message update rule equation with mean-field assumption:

\[\mu(x) = \exp \int q(y) q(z) \log f(x, y, z) \mathrm{d}y \mathrm{d}z\]

We see that in this setting, we do not need messages $\mu(y)$ and $\mu(z)$, but only the marginals $q(y)$ and $q(z)$. The purpose of a functional dependencies pipeline is to determine functional dependencies (a set of messages or marginals) that are needed to compute a single message. By default, ReactiveMP.jl uses so-called DefaultFunctionalDependencies that correctly implements belief-propagation and variational message passing schemes (including both mean-field and structured factorisations). The full list of built-in pipelines is presented below:

ReactiveMP.RequireMessageFunctionalDependencies — Type
RequireMessageFunctionalDependencies(indices::Tuple, start_with::Tuple)

The same as DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the inbound message on the this edge.

Arguments

  • indices::Tuple, tuple of integers, which indicates what edges should require inbound messages
  • start_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial inbound messages for edges in indices

Note: start_with uses setmessage! mechanism, hence, it can be visible by other listeners on the same edge. Explicit call to setmessage! overwrites whatever has been passed in start_with.

@model macro accepts a simplified construction of this pipeline:

@model function some_model()
+    # ...
+    y ~ NormalMeanVariance(x, τ) where {
+        pipeline = RequireMessage(x = vague(NormalMeanPrecision),     τ)
+                                  # ^^^                               ^^^
+                                  # request 'inbound' for 'x'         we may do the same for 'τ',
+                                  # and initialise with `vague(...)`  but here we skip initialisation
+    }
+    # ...
+end

Deprecation warning: RequireInboundFunctionalDependencies has been deprecated in favor of RequireMessageFunctionalDependencies.

See also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMarginalFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies

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ReactiveMP.RequireMarginalFunctionalDependencies — Type
RequireMarginalFunctionalDependencies(indices::Tuple, start_with::Tuple)

Similar to DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the posterior marginal on that edge.

Arguments

  • indices::Tuple, tuple of integers, which indicates what edges should require their own marginals
  • start_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial marginal for edges in indices

Note: start_with uses the setmarginal! mechanism, hence it can be visible to other listeners on the same edge. Explicit calls to setmarginal! overwrites whatever has been passed in start_with.

@model macro accepts a simplified construction of this pipeline:

@model function some_model()
+    # ...
+    y ~ NormalMeanVariance(x, τ) where {
+        pipeline = RequireMarginal(x = vague(NormalMeanPrecision),     τ)
+                                   # ^^^                               ^^^
+                                   # request 'marginal' for 'x'        we may do the same for 'τ',
+                                   # and initialise with `vague(...)`  but here we skip initialisation
+    }
+    # ...
+end

Note: The simplified construction in @model macro syntax is only available in GraphPPL.jl of version >2.2.0.

See also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMessageFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies

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ReactiveMP.RequireEverythingFunctionalDependencies — Type

RequireEverythingFunctionalDependencies

This pipeline specifies that in order to compute a message of some edge update rules request everything that is available locally. This includes all inbound messages (including on the same edge) and marginals over all local edge-clusters (this may or may not include marginals on single edges, depends on the local factorisation constraint).

See also: DefaultFunctionalDependencies, RequireMessageFunctionalDependencies, RequireMarginalFunctionalDependencies

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Node traits

Each factor node has to define the ReactiveMP.as_node_functional_form trait function and to specify a ReactiveMP.ValidNodeFunctionalForm singleton as a return object. By default ReactiveMP.as_node_functional_form returns ReactiveMP.UndefinedNodeFunctionalForm. Objects that do not specify this property correctly cannot be used in model specification.

Note

@node macro does that automatically

diff --git a/v3.13.3/lib/rules/rules/index.html b/v3.13.3/lib/rules/rules/index.html new file mode 100644 index 000000000..d64b64bae --- /dev/null +++ b/v3.13.3/lib/rules/rules/index.html @@ -0,0 +1,38 @@ + +Message update rules · ReactiveMP.jl
GitHub

Rules implementation

Message update rules

ReactiveMP.rule — Function
rule(fform, on, vconstraint, mnames, messages, qnames, marginals, meta, __node)

This function is used to compute an outbound message for a given node

Arguments

  • fform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)
  • on: Outbound interface's tag for which a message has to be computed, e.g. ::Val{:out} or ::Val{:μ}
  • vconstraint: Variable constraints for an outbound interface, e.g. Marginalisation or MomentMatching
  • mnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • messages: Tuple of message of the same length as mnames used to compute an outbound message
  • qnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • marginals: Tuple of marginals of the same length as qnames used to compute an outbound message
  • meta: Extra meta information
  • addons: Extra addons information
  • __node: Node reference

For all available rules, see ReactiveMP.print_rules_table().

See also: @rule, marginalrule, @marginalrule

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ReactiveMP.@rule — Macro
@rule NodeType(:Edge, Constraint) (Arguments..., [ meta::MetaType ]) = begin
+    # rule body
+    return ...
+end

The @rule macro help to define new methods for the rule function. It works particularly well in combination with the @node macro. It has a specific structure, which must specify:

  • NodeType: must be a valid Julia type. If some attempt to define a rule for a Julia function (for example +), use typeof(+)
  • Edge: edge label, usually edge labels are defined with the @node macro
  • Constrain: DEPRECATED, please just use the Marginalisation label
  • Arguments: defines a list of the input arguments for the rule
    • m_* prefix indicates that the argument is of type Message from the edge *
    • q_* prefix indicates that the argument is of type Marginal on the edge *
  • Meta::MetaType - optionally, a user can specify a Meta object of type MetaType. This can be useful is some attempts to try different rules with different approximation methods or if the rule itself requires some temporary storage or cache. The default meta is nothing.

Here are various examples of the @rule macro usage:

  1. Belief-Propagation (or Sum-Product) message update rule for the NormalMeanVariance node toward the :μ edge with the Marginalisation constraint. Input arguments are m_out and m_v, which are the messages from the corresponding edges out and v and have the type PointMass.
@rule NormalMeanVariance(:μ, Marginalisation) (m_out::PointMass, m_v::PointMass) = NormalMeanVariance(mean(m_out), mean(m_v))
  1. Mean-field message update rule for the NormalMeanVariance node towards the :μ edge with the Marginalisation constraint. Input arguments are q_out and q_v, which are the marginals on the corresponding edges out and v of type Any.
@rule NormalMeanVariance(:μ, Marginalisation) (q_out::Any, q_v::Any) = NormalMeanVariance(mean(q_out), mean(q_v))
  1. Structured Variational message update rule for the NormalMeanVariance node towards the :out edge with the Marginalisation constraint. Input arguments are m_μ, which is a message from the μ edge of type UnivariateNormalDistributionsFamily, and q_v, which is a marginal on the v edge of type Any.
@rule NormalMeanVariance(:out, Marginalisation) (m_μ::UnivariateNormalDistributionsFamily, q_v::Any) = begin
+    m_μ_mean, m_μ_cov = mean_cov(m_μ)
+    return NormalMeanVariance(m_μ_mean, m_μ_cov + mean(q_v))
+end

See also: rule, marginalrule, [@marginalrule], @call_rule

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ReactiveMP.@call_rule — Macro
@call_rule NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])

The @call_rule macro helps to call the rule method with an easier syntax. The structure of the macro is almost the same as in the @rule macro, but there is no begin ... end block, but instead each argument must have a specified value with the = operator.

The @call_rule accepts optional list of options before the functional form specification, for example:

@call_rule [ return_addons = true ] NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])

The list of available options is:

  • return_addons - forces the @call_rule to return the tuple of (result, addons)

See also: @rule, rule, @call_marginalrule

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ReactiveMP.print_rules_table — Function
Prints a table of all message passing update rules in ReactiveMP.
+
+Use `Markdown.parse` on the output of this function to get a prettified table.
source

Marginal update rules

ReactiveMP.marginalrule — Function
marginalrule(fform, on, mnames, messages, qnames, marginals, meta, __node)

This function is used to compute a local joint marginal for a given node

Arguments

  • fform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)
  • on: Local joint marginal tag , e.g. ::Val{ :mean_precision } or ::Val{ :out_mean_precision }
  • mnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • messages: Tuple of message of the same length as mnames used to compute an outbound message
  • qnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }
  • marginals: Tuple of marginals of the same length as qnames used to compute an outbound message
  • meta: Extra meta information
  • __node: Node reference

See also: rule, @rule @marginalrule

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ReactiveMP.@marginalrule — Macro
@marginalrule NodeType(:Cluster) (Arguments..., [ meta::MetaType ]) = begin
+    # rule body
+    return ...
+end

The @marginalrule macro help to define new methods for the marginalrule function. It works particularly well in combination with the @node macro. It has a specific structure, which must specify:

  • NodeType: must be a valid Julia type. If some attempt to define a rule for a Julia function (for example +), use typeof(+)
  • Cluster: edge cluster that contains joined edge labels with the _ symbol. Usually edge labels are defined with the @node macro
  • Arguments: defines a list of the input arguments for the rule
    • m_* prefix indicates that the argument is of type Message from the edge *
    • q_* prefix indicates that the argument is of type Marginal on the edge *
  • Meta::MetaType - optionally, a user can specify a Meta object of type MetaType. This can be useful is some attempts to try different rules with different approximation methods or if the rule itself requires some temporary storage or cache. The default meta is nothing.

The @marginalrule can return a NamedTuple in the return statement. This would indicate some variables in the joint marginal for the Cluster are independent and the joint itself is factorised. For example if some attempts to compute a marginal for the q(x, y) it is possible to return (x = ..., y = ...) as the result of the computation to indicate that q(x, y) = q(x)q(y).

Here are various examples of the @marginalrule macro usage:

  1. Marginal computation rule around the NormalMeanPrecision node for the q(out, μ). The rule accepts arguments m_out and m_μ, which are the messages

from the out and μ edges respectively, and q_τ which is the marginal on the edge τ.

@marginalrule NormalMeanPrecision(:out_μ) (m_out::UnivariateNormalDistributionsFamily, m_μ::UnivariateNormalDistributionsFamily, q_τ::Any) = begin
+    xi_out, W_out = weightedmean_precision(m_out)
+    xi_μ, W_μ     = weightedmean_precision(m_μ)
+
+    W_bar = mean(q_τ)
+
+    W  = [W_out+W_bar -W_bar; -W_bar W_μ+W_bar]
+    xi = [xi_out; xi_μ]
+
+    return MvNormalWeightedMeanPrecision(xi, W)
+end
  1. Marginal computation rule around the NormalMeanPrecision node for the q(out, μ). The rule accepts arguments m_out and m_μ, which are the messages from the

out and μ edges respectively, and q_τ which is the marginal on the edge τ. In this example the result of the computation is a NamedTuple

@marginalrule NormalMeanPrecision(:out_μ) (m_out::PointMass, m_μ::UnivariateNormalDistributionsFamily, q_τ::Any) = begin
+    return (out = m_out, μ = prod(ClosedProd(), NormalMeanPrecision(mean(m_out), mean(q_τ)), m_μ))
+end
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ReactiveMP.@call_marginalrule — Macro
@call_marginalrule NodeType(:edge) (argument1 = value1, argument2 = value2, ..., [ meta = ... ])

The @call_marginalrule macro helps to call the marginalrule method with an easier syntax. The structure of the macro is almost the same as in the @marginalrule macro, but there is no begin ... end block, but instead each argument must have a specified value with the = operator.

See also: @marginalrule, marginalrule, @call_rule

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Testing utilities for the update rules

ReactiveMP.@test_rules — Macro
@test_rules [options] rule [ test_entries... ]

The @test_rules macro generates test cases for message update rules for probabilistic programming models that follow the "message passing" paradigm. It takes a rule specification as input and generates a set of tests based on that specification. This macro is provided by ReactiveMP.

Note: The Test module must be imported explicitly. The @test_rules macro tries to use the @test macro, which must be defined globally.

Arguments

The macro takes three arguments:

  • options: An optional argument that specifies the options for the test generation process. See below for details.
  • rule: A rule specification in the same format as the @rule macro, e.g. Beta(:out, Marginalisation) or NormalMeanVariance(:μ, Marginalisation).
  • test_entries: An array of named tuples (input = ..., output = ...). The input entry has the same format as the input for the @rule macro. The output entry specifies the expected output.

Options

The following options are available:

  • check_type_promotion: By default, this option is set to false. If set to true, the macro generates an extensive list of extra tests that aim to check the correct type promotion within the tests. For example, if all inputs are of type Float32, then the expected output should also be of type Float32. See the paramfloattype and convert_paramfloattype functions for details.
  • atol: Sets the desired accuracy for the tests. The tests use the custom_rule_isapprox function from ReactiveMP to check if outputs are approximately the same. This argument can be either a single number or an array of key => value pairs.
  • extra_float_types: A set of extra float types to be used in the check_type_promotion tests. This argument has no effect if check_type_promotion is set to false.

The default values for the atol option are:

  • Float32: 1e-4
  • Float64: 1e-6
  • BigFloat: 1e-8

Examples


+@test_rules [check_type_promotion = true] Beta(:out, Marginalisation) [
+    (input = (m_a = PointMass(1.0), m_b = PointMass(2.0)), output = Beta(1.0, 2.0)),
+    (input = (m_a = PointMass(2.0), m_b = PointMass(2.0)), output = Beta(2.0, 2.0)),
+    (input = (m_a = PointMass(3.0), m_b = PointMass(3.0)), output = Beta(3.0, 3.0))
+]
+
+@test_rules [check_type_promotion = true] Beta(:out, Marginalisation) [
+    (input = (q_a = PointMass(1.0), q_b = PointMass(2.0)), output = Beta(1.0, 2.0)),
+    (input = (q_a = PointMass(2.0), q_b = PointMass(2.0)), output = Beta(2.0, 2.0)),
+    (input = (q_a = PointMass(3.0), q_b = PointMass(3.0)), output = Beta(3.0, 3.0))
+]

See also: ReactiveMP.@test_marginalrules

source

Table of available update rules

using ReactiveMP, Markdown
+Markdown.parse(ReactiveMP.print_rules_table())
NodeOutputInputsMeta
*inμ(A) :: BayesBase.PointMass{<:LinearAlgebra.UniformScaling}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
*inμ(A) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: BayesBase.PointMass{<:Real}
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecision
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: ExponentialFamily.MvNormalMeanPrecision
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: ExponentialFamily.MvNormalMeanCovariance
*inμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*inμ(out) :: Distributions.UnivariateDistributionUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: Distributions.UnivariateDistribution
*inμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*inμ(out) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass
*inμ(out) :: ExponentialFamily.GammaDistributionsFamilyUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:Real}
*inμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractMatrix}
*inμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractVector}
*inμ(out) :: FUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:Real}
*inμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractMatrix}
*inμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(A) :: BayesBase.PointMass{<:AbstractVector}
*outμ(A) :: Distributions.UnivariateDistributionUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Distributions.UnivariateDistribution
*outμ(A) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*outμ(A) :: BayesBase.PointMass{<:LinearAlgebra.UniformScaling}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
*outμ(A) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.MvNormalWeightedMeanPrecision
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.MvNormalMeanPrecision
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.MvNormalMeanCovariance
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*outμ(A) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass
*outμ(A) :: BayesBase.PointMass{<:Real}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: ExponentialFamily.GammaDistributionsFamily
*outμ(A) :: ExponentialFamily.GammaDistributionsFamilyUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*outμ(A) :: BayesBase.PointMass{<:AbstractMatrix}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: F
*outμ(A) :: FUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractMatrix}
*outμ(A) :: BayesBase.PointMass{<:AbstractVector}Union{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*outμ(A) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractVector}
*Aμ(out) :: Distributions.UnivariateDistributionUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Distributions.UnivariateDistribution
*Aμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
*Aμ(out) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass
*Aμ(out) :: ExponentialFamily.GammaDistributionsFamilyUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*Aμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractMatrix}
*Aμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractVector}
*Aμ(out) :: FUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:Real}
*Aμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractMatrix}
*Aμ(out) :: ExponentialFamily.MvNormalMeanCovarianceUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in) :: BayesBase.PointMass{<:AbstractVector}
+in2μ(out) :: Any
μ(in1) :: Any
-in2μ(out) :: Any
μ(in1) :: Any
-in1μ(out) :: Any
μ(in2) :: Any
-outμ(in1) :: Any
μ(in2) :: Any
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision
+in1μ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T1, M, P} where {M<:AbstractVector{T1}, P<:AbstractMatrix{T1}}Nothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T1, M, P} where {M<:AbstractVector{T1}, P<:AbstractMatrix{T1}}Nothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T2, M, P} where {M<:AbstractVector{T2}, P<:AbstractMatrix{T2}}
+in1μ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+in1μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+in1μ(out) :: ExponentialFamily.MvNormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalMeanPrecision
+in1μ(out) :: ExponentialFamily.NormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.NormalMeanPrecision
+in1μ(out) :: BayesBase.PointMassNothing
μ(in2) :: BayesBase.PointMass
+in1μ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+in1μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision
+outμ(in1) :: ExponentialFamily.MvNormalWeightedMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T1, M, P} where {M<:AbstractVector{T1}, P<:AbstractMatrix{T1}}Nothing
μ(in2) :: ExponentialFamily.MvNormalWeightedMeanPrecision{T2, M, P} where {M<:AbstractVector{T2}, P<:AbstractMatrix{T2}}
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.MvNormalMeanPrecision
+outμ(in1) :: ExponentialFamily.MvNormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: ExponentialFamily.NormalMeanPrecision
+outμ(in1) :: ExponentialFamily.NormalMeanPrecisionNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: BayesBase.PointMassNothing
μ(in2) :: BayesBase.PointMass
+outμ(in1) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(in2) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
+outμ(in1) :: Distributions.DistributionNothing
μ(in2) :: Distributions.Distribution
dotin2μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in1) :: BayesBase.PointMass
dotin1μ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in2) :: BayesBase.PointMass
dotoutμ(in1) :: BayesBase.PointMassUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in2) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
dotoutμ(in1) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TUnion{Nothing, MatrixCorrectionTools.AbstractCorrectionStrategy}
μ(in2) :: BayesBase.PointMass
ExponentialFamily.MatrixDirichletoutq(a) :: BayesBase.PointMassNothing
Distributions.Dirichletoutq(a) :: BayesBase.PointMass{<:AbstractVector}Nothing
ExponentialFamily.MatrixDirichletoutμ(a) :: BayesBase.PointMassNothing
Distributions.Dirichletoutμ(a) :: BayesBase.PointMass{<:AbstractVector}Nothing
ContinuousTransitionWq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
ContinuousTransitionaq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
q(W) :: Any
ContinuousTransitionxμ(y) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
q(W) :: Any
ContinuousTransitionyμ(x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TContinuousTransitionMeta
q(a) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
q(W) :: Any
ORoutμ(in1) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
ANDoutμ(in1) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
ORin2μ(out) :: Distributions.BernoulliNothing
μ(in1) :: Distributions.Bernoulli
ANDin2μ(out) :: Distributions.BernoulliNothing
μ(in1) :: Distributions.Bernoulli
ORin1μ(out) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
ANDin1μ(out) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
Distributions.Betaoutq(a) :: BayesBase.PointMassNothing
q(b) :: BayesBase.PointMass
Distributions.Uniformoutq(a) :: BayesBase.PointMassNothing
q(b) :: BayesBase.PointMass
Distributions.Betaoutμ(a) :: BayesBase.PointMassNothing
μ(b) :: BayesBase.PointMass
Distributions.Uniformoutμ(a) :: BayesBase.PointMassNothing
μ(b) :: BayesBase.PointMass
Distributions.Bernoullipq(out) :: Distributions.Categorical{P} where P<:RealNothing
Distributions.Bernoullipq(out) :: Distributions.BernoulliNothing
Distributions.Bernoullipq(out) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realpq(out) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realpq(out) :: AnyNothing
Distributions.Bernoullipμ(out) :: BayesBase.PointMassNothing
Distributions.Bernoullioutq(p) :: AnyNothing
Distributions.Bernoullioutq(p) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutq(p) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutq(p) :: Distributions.DirichletNothing
Distributions.Bernoullioutμ(p) :: BayesBase.PointMassNothing
Distributions.Bernoullioutμ(p) :: Distributions.BetaNothing
Distributions.Categorical{P} where P<:Realoutμ(p) :: BayesBase.PointMassNothing
Distributions.Categorical{P} where P<:Realoutμ(p) :: Distributions.DirichletNothing
InverseWishartoutq(ν) :: AnyNothing
q(S) :: Any
Distributions.Wishartoutq(ν) :: AnyNothing
q(S) :: Any
InverseWishartoutμ(ν) :: BayesBase.PointMassNothing
q(S) :: Any
Distributions.Wishartoutμ(ν) :: BayesBase.PointMassNothing
q(S) :: Any
InverseWishartoutμ(S) :: BayesBase.PointMassNothing
q(ν) :: Any
Distributions.Wishartoutμ(S) :: BayesBase.PointMassNothing
q(ν) :: Any
InverseWishartoutμ(ν) :: BayesBase.PointMassNothing
μ(S) :: BayesBase.PointMass
Distributions.Wishartoutμ(ν) :: BayesBase.PointMassNothing
μ(S) :: BayesBase.PointMass
BIFMHelperoutq(in) :: AnyNothing
Probitoutq(in) :: BayesBase.PointMassUnion{Nothing, ProbitMeta}
BIFMHelperinμ(out) :: AnyNothing
NOTinμ(out) :: Distributions.BernoulliNothing
Probitinμ(out) :: Union{BayesBase.PointMass, Distributions.Bernoulli}Union{Nothing, ProbitMeta}
GammaMixture{N}outq(switch) :: AnyNothing
q(a) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(b) :: ExponentialFamily.GammaDistributionsFamily
GammaMixturebq(out) :: AnyNothing
q(switch) :: Any
q(a) :: Any
GammaMixtureaq(out) :: AnyNothing
q(switch) :: Any
q(b) :: ExponentialFamily.GammaDistributionsFamily
GammaMixture{N}switchq(out) :: AnyNothing
q(a) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(b) :: ExponentialFamily.GammaDistributionsFamily
Distributions.InverseGammaoutq(α) :: AnyNothing
q(θ) :: Any
Distributions.Gammaoutq(α) :: AnyNothing
q(θ) :: Any
Distributions.InverseGammaoutμ(α) :: BayesBase.PointMassNothing
μ(θ) :: BayesBase.PointMass
Distributions.Gammaoutμ(α) :: BayesBase.PointMassNothing
μ(θ) :: BayesBase.PointMass
ARyq(x) :: AnyARMeta
q(θ) :: Any
q(γ) :: Any
ARyμ(x) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TARMeta
q(θ) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
q(γ) :: Any
ARθq(y) :: AnyARMeta
q(x) :: Any
q(γ) :: Any
ARθq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TARMeta
q(γ) :: Any
ARxq(y) :: AnyARMeta
q(θ) :: Any
q(γ) :: Any
ARxμ(y) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TARMeta
q(θ) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
q(γ) :: Any
ARγq(y) :: AnyARMeta
q(x) :: Any
q(θ) :: Any
ARγq(y_x) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TARMeta
q(θ) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
ExponentialFamily.NormalMeanVariancevq(out_μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
ExponentialFamily.NormalMeanVariancevq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.NormalMeanVariancevμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
ExponentialFamily.NormalMeanVariancevμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(μ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVariancevμ(out) :: BayesBase.PointMassNothing
μ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
ExponentialFamily.NormalMeanVarianceμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceμμ(out) :: BayesBase.PointMassNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
q(v) :: Any
ExponentialFamily.NormalMeanVarianceμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμμ(out) :: BayesBase.PointMassNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμq(out) :: BayesBase.PointMassNothing
q(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceμq(out) :: AnyNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: BayesBase.PointMassNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutq(μ) :: BayesBase.PointMassNothing
q(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceoutq(μ) :: AnyNothing
q(v) :: Any
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(v) :: BayesBase.PointMass
ExponentialFamily.NormalMeanVarianceoutμ(μ) :: BayesBase.PointMassNothing
μ(v) :: BayesBase.PointMass
NOToutμ(in) :: Distributions.BernoulliNothing
Probitoutμ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, ProbitMeta}
Probitoutμ(in) :: BayesBase.PointMassUnion{Nothing, ProbitMeta}
Flowinμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TFlowMeta{M, <:Unscented}
Flowinμ(out) :: ExponentialFamily.MvNormalWeightedMeanPrecisionFlowMeta{M, <:Linearization}
Flowinμ(out) :: ExponentialFamily.MvNormalMeanPrecisionFlowMeta{M, <:Linearization}
Flowinμ(out) :: ExponentialFamily.MvNormalMeanCovarianceFlowMeta{M, <:Linearization}
Flowoutμ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TFlowMeta{M, <:Unscented}
Flowoutμ(in) :: ExponentialFamily.MvNormalWeightedMeanPrecisionFlowMeta{M, <:Linearization}
Flowoutμ(in) :: ExponentialFamily.MvNormalMeanPrecisionFlowMeta{M, <:Linearization}
Flowoutμ(in) :: ExponentialFamily.MvNormalMeanCovarianceFlowMeta{M, <:Linearization}
NormalMixture{N}outq(switch) :: AnyNothing
q(m) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(p) :: ManyOf{<:Tuple{Vararg{Any, N}}}
NormalMixturepq(out) :: AnyNothing
q(switch) :: Any
q(m) :: Any
NormalMixturemq(out) :: AnyNothing
q(switch) :: Any
q(p) :: Any
NormalMixture{N}switchq(out) :: AnyNothing
q(m) :: ManyOf{<:Tuple{Vararg{Any, N}}}
q(p) :: ManyOf{<:Tuple{Vararg{Any, N}}}
GCVyq(x) :: AnyUnion{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
GCVyμ(x) :: Union{ReactiveMP.ExponentialLinearQuadratic, ExponentialFamily.UnivariateNormalDistributionsFamily, ExponentialFamily.UnivariateGaussianDistributionsFamily}Union{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
GCVzq(y) :: AnyGCVMetadata
q(x) :: Any
q(κ) :: Any
q(ω) :: Any
GCVzq(y_x) :: AnyGCVMetadata
q(κ) :: Any
q(ω) :: Any
GCVκq(y) :: AnyGCVMetadata
q(x) :: Any
q(z) :: Any
q(ω) :: Any
GCVκq(y_x) :: AnyGCVMetadata
q(z) :: Any
q(ω) :: Any
GCVωq(y) :: AnyGCVMetadata
q(x) :: Any
q(z) :: Any
q(κ) :: Any
GCVωq(y_x) :: AnyGCVMetadata
q(z) :: Any
q(κ) :: Any
GCVxq(y) :: AnyUnion{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
GCVxμ(y) :: Union{ReactiveMP.ExponentialLinearQuadratic, ExponentialFamily.UnivariateNormalDistributionsFamily, ExponentialFamily.UnivariateGaussianDistributionsFamily}Union{Nothing, GCVMetadata}
q(z) :: Any
q(κ) :: Any
q(ω) :: Any
BIFMzprevμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
μ(znext) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
BIFMznextμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
μ(zprev) :: BayesBase.TerminalProdArgument{<:Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T}
BIFMoutμ(in) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(zprev) :: BayesBase.TerminalProdArgument{<:Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T}
μ(znext) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
BIFMinμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TBIFMMeta
μ(zprev) :: BayesBase.TerminalProdArgument{<:Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T}
μ(znext) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where T
ExponentialFamily.MvNormalMeanCovarianceΣq(out_μ) :: AnyNothing
ExponentialFamily.MvNormalMeanCovarianceΣq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: BayesBase.PointMassNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutq(μ) :: BayesBase.PointMassNothing
q(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceoutq(μ) :: AnyNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceoutμ(μ) :: BayesBase.PointMassNothing
μ(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: BayesBase.PointMassNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceμq(out) :: BayesBase.PointMassNothing
q(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceμq(out) :: AnyNothing
q(Σ) :: Any
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Σ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanCovarianceμμ(out) :: BayesBase.PointMassNothing
μ(Σ) :: BayesBase.PointMass
Transitioninq(out) :: BayesBase.PointMassNothing
q(a) :: BayesBase.PointMass
Transitioninq(out) :: AnyNothing
q(a) :: ExponentialFamily.MatrixDirichlet
Transitioninμ(out) :: Distributions.Categorical{P} where P<:Real
q(a) :: BayesBase.PointMass
Transitioninμ(out) :: Distributions.Categorical{P} where P<:RealNothing
q(a) :: ExponentialFamily.MatrixDirichlet
Transitioninμ(out) :: Distributions.Categorical{P} where P<:RealNothing
μ(a) :: BayesBase.PointMass
Transitionoutμ(in) :: Distributions.DiscreteNonParametric
q(a) :: BayesBase.PointMass
Transitionoutμ(in) :: Distributions.Categorical{P} where P<:RealNothing
q(a) :: Distributions.Distribution{Distributions.Matrixvariate, Distributions.Continuous}
Transitionoutq(in) :: Distributions.Categorical{P} where P<:RealNothing
q(a) :: Any
Transitionoutq(in) :: BayesBase.PointMassNothing
q(a) :: BayesBase.PointMass
Transitionoutμ(in) :: Distributions.Categorical{P} where P<:RealNothing
μ(a) :: BayesBase.PointMass
Transitionaq(out_in) :: BayesBase.ContingencyNothing
Transitionaq(out) :: AnyNothing
q(in) :: Distributions.Categorical{P} where P<:Real
MvNormalMeanScalePrecisionγq(out_μ) :: AnyNothing
MvNormalMeanScalePrecisionγq(out) :: AnyNothing
q(μ) :: Any
MvNormalMeanScalePrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(γ) :: Any
MvNormalMeanScalePrecisionμq(out) :: AnyNothing
q(γ) :: Any
MvNormalMeanScalePrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(γ) :: Any
MvNormalMeanScalePrecisionoutq(μ) :: AnyNothing
q(γ) :: Any
Mixtureswitchμ(out) :: AnyNothing
μ(inputs) :: ManyOf{<:Tuple{Vararg{Any, N}}}
Mixtureoutμ(inputs) :: ManyOf{<:Tuple{Vararg{Any, N}}}Nothing
q(switch) :: BayesBase.PointMass
Mixtureoutμ(switch) :: AnyNothing
μ(inputs) :: ManyOf{<:Tuple{Vararg{Any, N}}}
Mixtureinputsμ(out) :: AnyNothing
q(switch) :: BayesBase.PointMass
Mixtureinputsμ(out) :: AnyNothing
μ(switch) :: Any
ExponentialFamily.NormalMeanPrecisionτq(out_μ) :: AnyNothing
ExponentialFamily.NormalMeanPrecisionτq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμμ(out) :: ReactiveMP.ExponentialLinearQuadratic
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμq(out) :: BayesBase.PointMassNothing
q(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionμq(out) :: AnyNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutq(μ) :: BayesBase.PointMassNothing
q(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionoutq(μ) :: AnyNothing
q(τ) :: Any
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TNothing
μ(τ) :: BayesBase.PointMass
ExponentialFamily.NormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
μ(τ) :: BayesBase.PointMass
HalfNormaloutq(v) :: BayesBase.PointMassNothing
ExponentialFamily.MvNormalMeanPrecisionΛq(out_μ) :: AnyNothing
ExponentialFamily.MvNormalMeanPrecisionΛq(out) :: AnyNothing
q(μ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Distributions.Wishart
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutq(μ) :: BayesBase.PointMassNothing
q(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionoutq(μ) :: AnyNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionoutμ(μ) :: BayesBase.PointMassNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Distributions.Wishart
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionμq(out) :: BayesBase.PointMassNothing
q(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionμq(out) :: AnyNothing
q(Λ) :: Any
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: Union{ExponentialFamily.MultivariateNormalDistributionsFamily{T}, ExponentialFamily.MultivariateGaussianDistributionsFamily{T}} where TNothing
μ(Λ) :: BayesBase.PointMass
ExponentialFamily.MvNormalMeanPrecisionμμ(out) :: BayesBase.PointMassNothing
μ(Λ) :: BayesBase.PointMass
DeltaFnoutq(ins) :: BayesBase.FactorizedJoint{P}DeltaMeta{M}
DeltaFnoutq(ins) :: BayesBase.FactorizedJointDeltaMeta{M}
DeltaFnoutμ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M}
DeltaFnoutμ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M}
DeltaFninμ(in) :: AnyDeltaMeta{M}
q(ins) :: BayesBase.FactorizedJoint
DeltaFninμ(in) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, Nothing}
q(ins) :: ExponentialFamily.JointNormal
DeltaFninμ(in) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
q(ins) :: ExponentialFamily.JointNormal
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Nothing
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Nothing
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
DeltaFninμ(out) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where TDeltaMeta{M, I}
μ(ins) :: Union{ExponentialFamily.NormalDistributionsFamily{T}, ExponentialFamily.GaussianDistributionsFamily{T}} where T
SoftDotγq(y) :: AnyNothing
q(θ) :: Any
q(x) :: Any
SoftDotθq(y) :: AnyNothing
q(x) :: Any
q(γ) :: Any
SoftDotxq(y) :: AnyNothing
q(θ) :: Any
q(γ) :: Any
SoftDotyq(θ) :: AnyNothing
q(x) :: Any
q(γ) :: Any
Distributions.Uniformoutμ(b) :: BayesBase.PointMassNothing
q(a) :: BayesBase.PointMass
Distributions.Uniformoutμ(a) :: BayesBase.PointMassNothing
q(b) :: BayesBase.PointMass
Poissonoutq(l) :: ExponentialFamily.GammaDistributionsFamilyNothing
Poissonoutμ(l) :: BayesBase.PointMassNothing
Poissonlq(out) :: AnyNothing
Poissonlμ(out) :: BayesBase.PointMassNothing
Probitinμ(out) :: Union{BayesBase.PointMass, Distributions.Bernoulli}Union{Nothing, ProbitMeta}
μ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where T
Probitinμ(in) :: Union{ExponentialFamily.UnivariateNormalDistributionsFamily{T}, ExponentialFamily.UnivariateGaussianDistributionsFamily{T}} where TUnion{Nothing, ProbitMeta}
q(out) :: BayesBase.PointMass
Probitinq(out) :: BayesBase.PointMassUnion{Nothing, ProbitMeta}
ExponentialFamily.GammaShapeRateαq(out) :: Distributions.GammaNothing
q(β) :: Distributions.Gamma
ExponentialFamily.GammaShapeRateoutq(α) :: BayesBase.PointMassNothing
q(β) :: BayesBase.PointMass
ExponentialFamily.GammaShapeRateoutμ(α) :: BayesBase.PointMassNothing
μ(β) :: BayesBase.PointMass
ExponentialFamily.MvNormalWeightedMeanPrecisionoutq(ξ) :: AnyNothing
q(Λ) :: Any
ExponentialFamily.MvNormalWeightedMeanPrecisionoutμ(ξ) :: BayesBase.PointMassNothing
μ(Λ) :: BayesBase.PointMass
outμ(in1) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
in2μ(out) :: Distributions.BernoulliNothing
μ(in1) :: Distributions.Bernoulli
in1μ(out) :: Distributions.BernoulliNothing
μ(in2) :: Distributions.Bernoulli
+
diff --git a/v3.13.3/search_index.js b/v3.13.3/search_index.js new file mode 100644 index 000000000..8d55bd0c1 --- /dev/null +++ b/v3.13.3/search_index.js @@ -0,0 +1,3 @@ +var documenterSearchIndex = {"docs": +[{"location":"lib/helpers/#lib-helpers","page":"Helper utils","title":"Helper utilities","text":"","category":"section"},{"location":"lib/helpers/","page":"Helper utils","title":"Helper utils","text":"ReactiveMP implements various structures/functions/methods as \"helper\" structures that might be useful in various contexts.","category":"page"},{"location":"lib/helpers/#lib-helpers-skip-index-iterator","page":"Helper utils","title":"SkipIndexIterator","text":"","category":"section"},{"location":"lib/helpers/","page":"Helper utils","title":"Helper utils","text":"ReactiveMP.SkipIndexIterator\nReactiveMP.skipindex","category":"page"},{"location":"lib/helpers/#ReactiveMP.SkipIndexIterator","page":"Helper utils","title":"ReactiveMP.SkipIndexIterator","text":"SkipIndexIterator\n\nA special type of iterator that simply iterates over internal iterator, but skips index skip.\n\nArguments\n\niterator: internal iterator\nskip: index to skip (integer)\n\nSee also: skipindex\n\n\n\n\n\n","category":"type"},{"location":"lib/helpers/#ReactiveMP.skipindex","page":"Helper utils","title":"ReactiveMP.skipindex","text":"skipindex(iterator, skip)\n\nCreation operator for SkipIndexIterator.\n\njulia> s = ReactiveMP.skipindex(1:3, 2)\n2-element ReactiveMP.SkipIndexIterator{Int64, UnitRange{Int64}}:\n 1\n 3\n\njulia> collect(s)\n2-element Vector{Int64}:\n 1\n 3\n\nSee also: SkipIndexIterator\n\n\n\n\n\n","category":"function"},{"location":"lib/helpers/#lib-helpers-functional-index","page":"Helper utils","title":"FunctionalIndex","text":"","category":"section"},{"location":"lib/helpers/","page":"Helper utils","title":"Helper utils","text":"ReactiveMP.FunctionalIndex","category":"page"},{"location":"lib/helpers/#ReactiveMP.FunctionalIndex","page":"Helper utils","title":"ReactiveMP.FunctionalIndex","text":"FunctionalIndex\n\nA special type of an index that represents a function that can be used only in pair with a collection. An example of a FunctionalIndex can be firstindex or lastindex, but more complex use cases are possible too, e.g. firstindex + 1. Important part of the implementation is that the resulting structure is isbitstype(...) = true, that allows to store it in parametric type as valtype.\n\nOne use case for this structure is to dispatch on and to replace begin or end (or more complex use cases, e.g. begin + 1) markers in constraints specification language.\n\n\n\n\n\n","category":"type"},{"location":"lib/rules/rules/#lib-rules","page":"Message update rules","title":"Rules implementation","text":"","category":"section"},{"location":"lib/rules/rules/#lib-message-rules","page":"Message update rules","title":"Message update rules","text":"","category":"section"},{"location":"lib/rules/rules/","page":"Message update rules","title":"Message update rules","text":"rule\n@rule\n@call_rule\nReactiveMP.print_rules_table","category":"page"},{"location":"lib/rules/rules/#ReactiveMP.rule","page":"Message update rules","title":"ReactiveMP.rule","text":"rule(fform, on, vconstraint, mnames, messages, qnames, marginals, meta, __node)\n\nThis function is used to compute an outbound message for a given node\n\nArguments\n\nfform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)\non: Outbound interface's tag for which a message has to be computed, e.g. ::Val{:out} or ::Val{:μ}\nvconstraint: Variable constraints for an outbound interface, e.g. Marginalisation or MomentMatching\nmnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }\nmessages: Tuple of message of the same length as mnames used to compute an outbound message\nqnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }\nmarginals: Tuple of marginals of the same length as qnames used to compute an outbound message\nmeta: Extra meta information\naddons: Extra addons information\n__node: Node reference\n\nFor all available rules, see ReactiveMP.print_rules_table().\n\nSee also: @rule, marginalrule, @marginalrule\n\n\n\n\n\n","category":"function"},{"location":"lib/rules/rules/#ReactiveMP.@rule","page":"Message update rules","title":"ReactiveMP.@rule","text":"@rule NodeType(:Edge, Constraint) (Arguments..., [ meta::MetaType ]) = begin\n # rule body\n return ...\nend\n\nThe @rule macro help to define new methods for the rule function. It works particularly well in combination with the @node macro. It has a specific structure, which must specify:\n\nNodeType: must be a valid Julia type. If some attempt to define a rule for a Julia function (for example +), use typeof(+)\nEdge: edge label, usually edge labels are defined with the @node macro\nConstrain: DEPRECATED, please just use the Marginalisation label\nArguments: defines a list of the input arguments for the rule\nm_* prefix indicates that the argument is of type Message from the edge *\nq_* prefix indicates that the argument is of type Marginal on the edge *\nMeta::MetaType - optionally, a user can specify a Meta object of type MetaType. This can be useful is some attempts to try different rules with different approximation methods or if the rule itself requires some temporary storage or cache. The default meta is nothing.\n\nHere are various examples of the @rule macro usage:\n\nBelief-Propagation (or Sum-Product) message update rule for the NormalMeanVariance node toward the :μ edge with the Marginalisation constraint. Input arguments are m_out and m_v, which are the messages from the corresponding edges out and v and have the type PointMass.\n\n@rule NormalMeanVariance(:μ, Marginalisation) (m_out::PointMass, m_v::PointMass) = NormalMeanVariance(mean(m_out), mean(m_v))\n\nMean-field message update rule for the NormalMeanVariance node towards the :μ edge with the Marginalisation constraint. Input arguments are q_out and q_v, which are the marginals on the corresponding edges out and v of type Any.\n\n@rule NormalMeanVariance(:μ, Marginalisation) (q_out::Any, q_v::Any) = NormalMeanVariance(mean(q_out), mean(q_v))\n\nStructured Variational message update rule for the NormalMeanVariance node towards the :out edge with the Marginalisation constraint. Input arguments are m_μ, which is a message from the μ edge of type UnivariateNormalDistributionsFamily, and q_v, which is a marginal on the v edge of type Any.\n\n@rule NormalMeanVariance(:out, Marginalisation) (m_μ::UnivariateNormalDistributionsFamily, q_v::Any) = begin\n m_μ_mean, m_μ_cov = mean_cov(m_μ)\n return NormalMeanVariance(m_μ_mean, m_μ_cov + mean(q_v))\nend\n\nSee also: rule, marginalrule, [@marginalrule], @call_rule\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#ReactiveMP.@call_rule","page":"Message update rules","title":"ReactiveMP.@call_rule","text":"@call_rule NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])\n\nThe @call_rule macro helps to call the rule method with an easier syntax. The structure of the macro is almost the same as in the @rule macro, but there is no begin ... end block, but instead each argument must have a specified value with the = operator.\n\nThe @call_rule accepts optional list of options before the functional form specification, for example:\n\n@call_rule [ return_addons = true ] NodeType(:edge, Constraint) (argument1 = value1, argument2 = value2, ..., [ meta = ..., addons = ... ])\n\nThe list of available options is:\n\nreturn_addons - forces the @call_rule to return the tuple of (result, addons)\n\nSee also: @rule, rule, @call_marginalrule\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#ReactiveMP.print_rules_table","page":"Message update rules","title":"ReactiveMP.print_rules_table","text":"Prints a table of all message passing update rules in ReactiveMP.\n\nUse `Markdown.parse` on the output of this function to get a prettified table.\n\n\n\n\n\n","category":"function"},{"location":"lib/rules/rules/#lib-marginal-rules","page":"Message update rules","title":"Marginal update rules","text":"","category":"section"},{"location":"lib/rules/rules/","page":"Message update rules","title":"Message update rules","text":"marginalrule\n@marginalrule\n@call_marginalrule","category":"page"},{"location":"lib/rules/rules/#ReactiveMP.marginalrule","page":"Message update rules","title":"ReactiveMP.marginalrule","text":"marginalrule(fform, on, mnames, messages, qnames, marginals, meta, __node)\n\nThis function is used to compute a local joint marginal for a given node\n\nArguments\n\nfform: Functional form of the node in form of a type of the node, e.g. ::Type{ <: NormalMeanVariance } or ::typeof(+)\non: Local joint marginal tag , e.g. ::Val{ :mean_precision } or ::Val{ :out_mean_precision }\nmnames: Ordered messages names in form of the Val type, eg. ::Val{ (:mean, :precision) }\nmessages: Tuple of message of the same length as mnames used to compute an outbound message\nqnames: Ordered marginal names in form of the Val type, eg. ::Val{ (:mean, :precision) }\nmarginals: Tuple of marginals of the same length as qnames used to compute an outbound message\nmeta: Extra meta information\n__node: Node reference\n\nSee also: rule, @rule @marginalrule\n\n\n\n\n\n","category":"function"},{"location":"lib/rules/rules/#ReactiveMP.@marginalrule","page":"Message update rules","title":"ReactiveMP.@marginalrule","text":"@marginalrule NodeType(:Cluster) (Arguments..., [ meta::MetaType ]) = begin\n # rule body\n return ...\nend\n\nThe @marginalrule macro help to define new methods for the marginalrule function. It works particularly well in combination with the @node macro. It has a specific structure, which must specify:\n\nNodeType: must be a valid Julia type. If some attempt to define a rule for a Julia function (for example +), use typeof(+)\nCluster: edge cluster that contains joined edge labels with the _ symbol. Usually edge labels are defined with the @node macro\nArguments: defines a list of the input arguments for the rule\nm_* prefix indicates that the argument is of type Message from the edge *\nq_* prefix indicates that the argument is of type Marginal on the edge *\nMeta::MetaType - optionally, a user can specify a Meta object of type MetaType. This can be useful is some attempts to try different rules with different approximation methods or if the rule itself requires some temporary storage or cache. The default meta is nothing.\n\nThe @marginalrule can return a NamedTuple in the return statement. This would indicate some variables in the joint marginal for the Cluster are independent and the joint itself is factorised. For example if some attempts to compute a marginal for the q(x, y) it is possible to return (x = ..., y = ...) as the result of the computation to indicate that q(x, y) = q(x)q(y).\n\nHere are various examples of the @marginalrule macro usage:\n\nMarginal computation rule around the NormalMeanPrecision node for the q(out, μ). The rule accepts arguments m_out and m_μ, which are the messages \n\nfrom the out and μ edges respectively, and q_τ which is the marginal on the edge τ.\n\n@marginalrule NormalMeanPrecision(:out_μ) (m_out::UnivariateNormalDistributionsFamily, m_μ::UnivariateNormalDistributionsFamily, q_τ::Any) = begin\n xi_out, W_out = weightedmean_precision(m_out)\n xi_μ, W_μ = weightedmean_precision(m_μ)\n\n W_bar = mean(q_τ)\n\n W = [W_out+W_bar -W_bar; -W_bar W_μ+W_bar]\n xi = [xi_out; xi_μ]\n\n return MvNormalWeightedMeanPrecision(xi, W)\nend\n\nMarginal computation rule around the NormalMeanPrecision node for the q(out, μ). The rule accepts arguments m_out and m_μ, which are the messages from the \n\nout and μ edges respectively, and q_τ which is the marginal on the edge τ. In this example the result of the computation is a NamedTuple\n\n@marginalrule NormalMeanPrecision(:out_μ) (m_out::PointMass, m_μ::UnivariateNormalDistributionsFamily, q_τ::Any) = begin\n return (out = m_out, μ = prod(ClosedProd(), NormalMeanPrecision(mean(m_out), mean(q_τ)), m_μ))\nend\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#ReactiveMP.@call_marginalrule","page":"Message update rules","title":"ReactiveMP.@call_marginalrule","text":"@call_marginalrule NodeType(:edge) (argument1 = value1, argument2 = value2, ..., [ meta = ... ])\n\nThe @call_marginalrule macro helps to call the marginalrule method with an easier syntax. The structure of the macro is almost the same as in the @marginalrule macro, but there is no begin ... end block, but instead each argument must have a specified value with the = operator.\n\nSee also: @marginalrule, marginalrule, @call_rule\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#lib-rules-tests","page":"Message update rules","title":"Testing utilities for the update rules","text":"","category":"section"},{"location":"lib/rules/rules/","page":"Message update rules","title":"Message update rules","text":"ReactiveMP.@test_rules\nReactiveMP.@test_marginalrules","category":"page"},{"location":"lib/rules/rules/#ReactiveMP.@test_rules","page":"Message update rules","title":"ReactiveMP.@test_rules","text":"@test_rules [options] rule [ test_entries... ]\n\nThe @test_rules macro generates test cases for message update rules for probabilistic programming models that follow the \"message passing\" paradigm. It takes a rule specification as input and generates a set of tests based on that specification. This macro is provided by ReactiveMP.\n\nNote: The Test module must be imported explicitly. The @test_rules macro tries to use the @test macro, which must be defined globally.\n\nArguments\n\nThe macro takes three arguments:\n\noptions: An optional argument that specifies the options for the test generation process. See below for details.\nrule: A rule specification in the same format as the @rule macro, e.g. Beta(:out, Marginalisation) or NormalMeanVariance(:μ, Marginalisation).\ntest_entries: An array of named tuples (input = ..., output = ...). The input entry has the same format as the input for the @rule macro. The output entry specifies the expected output.\n\nOptions\n\nThe following options are available:\n\ncheck_type_promotion: By default, this option is set to false. If set to true, the macro generates an extensive list of extra tests that aim to check the correct type promotion within the tests. For example, if all inputs are of type Float32, then the expected output should also be of type Float32. See the paramfloattype and convert_paramfloattype functions for details.\natol: Sets the desired accuracy for the tests. The tests use the custom_rule_isapprox function from ReactiveMP to check if outputs are approximately the same. This argument can be either a single number or an array of key => value pairs.\nextra_float_types: A set of extra float types to be used in the check_type_promotion tests. This argument has no effect if check_type_promotion is set to false.\n\nThe default values for the atol option are:\n\nFloat32: 1e-4\nFloat64: 1e-6\nBigFloat: 1e-8\n\nExamples\n\n\n@test_rules [check_type_promotion = true] Beta(:out, Marginalisation) [\n (input = (m_a = PointMass(1.0), m_b = PointMass(2.0)), output = Beta(1.0, 2.0)),\n (input = (m_a = PointMass(2.0), m_b = PointMass(2.0)), output = Beta(2.0, 2.0)),\n (input = (m_a = PointMass(3.0), m_b = PointMass(3.0)), output = Beta(3.0, 3.0))\n]\n\n@test_rules [check_type_promotion = true] Beta(:out, Marginalisation) [\n (input = (q_a = PointMass(1.0), q_b = PointMass(2.0)), output = Beta(1.0, 2.0)),\n (input = (q_a = PointMass(2.0), q_b = PointMass(2.0)), output = Beta(2.0, 2.0)),\n (input = (q_a = PointMass(3.0), q_b = PointMass(3.0)), output = Beta(3.0, 3.0))\n]\n\nSee also: ReactiveMP.@test_marginalrules\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#ReactiveMP.@test_marginalrules","page":"Message update rules","title":"ReactiveMP.@test_marginalrules","text":"@test_marginalrules [options] rule [ test_entries... ]\n\nEffectively the same as @test_rules, but for marginal computation rules. See the documentation for @test_rules for more info.\n\nSee also: ReactiveMP.@test_rules\n\n\n\n\n\n","category":"macro"},{"location":"lib/rules/rules/#lib-rules-table","page":"Message update rules","title":"Table of available update rules","text":"","category":"section"},{"location":"lib/rules/rules/","page":"Message update rules","title":"Message update rules","text":"using ReactiveMP, Markdown\nMarkdown.parse(ReactiveMP.print_rules_table())","category":"page"},{"location":"lib/message/#lib-message","page":"Messages","title":"Messages implementation","text":"","category":"section"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"In our message passing framework one of the most important concepts is the message (wow!). Messages flow along edges of a factor graph and hold information about the part of the graph that it originates from. Usually this information is in a form of a probability distribution. Two common messages are belief propagation messages and variational messages, with are computed differently as shown below.","category":"page"},{"location":"lib/message/#Abstract-message-type","page":"Messages","title":"Abstract message type","text":"","category":"section"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"Both belief propagation and variational messages are subtypes of a AbstractMessage supertype.","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"AbstractMessage","category":"page"},{"location":"lib/message/#ReactiveMP.AbstractMessage","page":"Messages","title":"ReactiveMP.AbstractMessage","text":"AbstractMessage\n\nAn abstract supertype for all concrete message types.\n\nSee also: Message\n\n\n\n\n\n","category":"type"},{"location":"lib/message/#lib-belief-propagation-message","page":"Messages","title":"Belief-Propagation (or Sum-Product) message","text":"","category":"section"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"(Image: message) Belief propagation message","category":"page"},{"location":"lib/message/#lib-variational-message","page":"Messages","title":"Variational message","text":"","category":"section"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"(Image: message) Variational message with structured factorisation q(x, y)q(z) assumption","category":"page"},{"location":"lib/message/#Message-type","page":"Messages","title":"Message type","text":"","category":"section"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"All messages are encoded with the type Message. ","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"Message\nReactiveMP.materialize!","category":"page"},{"location":"lib/message/#ReactiveMP.Message","page":"Messages","title":"ReactiveMP.Message","text":"Message{D, A} <: AbstractMessage\n\nMessage structure encodes a Belief Propagation message, which holds some data that usually a probability distribution, but can also be an arbitrary object. Message acts as a proxy structure to data object and proxies most of the statistical functions, e.g. mean, mode, cov etc.\n\nArguments\n\ndata::D: message always holds some data object associated with it\nis_clamped::Bool, specifies if this message is clamped\nis_initial::Bool, specifies if this message is initial\naddons::A, specifies the addons of the message\n\nExample\n\njulia> distribution = Gamma(10.0, 2.0)\nDistributions.Gamma{Float64}(α=10.0, θ=2.0)\n\njulia> message = Message(distribution, false, true, nothing)\nMessage(Distributions.Gamma{Float64}(α=10.0, θ=2.0))\n\njulia> mean(message) \n20.0\n\njulia> getdata(message)\nDistributions.Gamma{Float64}(α=10.0, θ=2.0)\n\njulia> is_clamped(message)\nfalse\n\njulia> is_initial(message)\ntrue\n\n\nSee also: AbstractMessage, ReactiveMP.materialize!\n\n\n\n\n\n","category":"type"},{"location":"lib/message/#ReactiveMP.materialize!","page":"Messages","title":"ReactiveMP.materialize!","text":"materialize!(message::AbstractMessage)\n\nMaterializes an abstract message and converts it to be of type Message.\n\nSee also: Message\n\n\n\n\n\n","category":"function"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"From an implementation point a view the Message structure does nothing but hold some data object and redirects most of the statistical related functions to that data object. However, this object is used extensively in Julia's multiple dispatch. Our implementation also uses extra is_initial and is_clamped fields to determine if product of two messages results in is_initial or is_clamped posterior marginal. The final field contains the addons. These contain additional information on top of the functional form of the distribution, such as its scaling or computation history.","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"using ReactiveMP, BayesBase, Distributions, ExponentialFamily","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"distribution = NormalMeanPrecision(0.0, 1.0)\nmessage = Message(distribution, false, true, nothing)","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"mean(message), precision(message)","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"logpdf(message, 1.0)","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"is_clamped(message), is_initial(message)","category":"page"},{"location":"lib/message/","page":"Messages","title":"Messages","text":"The user should not really interact with Message structure while working with ReactiveMP unless doing some advanced inference procedures that involve prediction.","category":"page"},{"location":"lib/nodes/nodes/#lib-node","page":"Overview","title":"Nodes implementation","text":"","category":"section"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"In the message passing framework, one of the most important concepts is a factor node. A factor node represents a local function in a factorised representation of a generative model.","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"note: Note\nTo quickly check the list of all available factor nodes that can be used in the model specification language, call ?make_node or Base.doc(make_node).","category":"page"},{"location":"lib/nodes/nodes/#lib-custom-node","page":"Overview","title":"Adding a custom node","text":"","category":"section"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"ReactiveMP.jl exports the @node macro that allows for quick definition of a factor node with a fixed number of edges. The interface is the following:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"struct MyNewCustomNode end\n\n@node MyNewCustomNode Stochastic [ x, y, z ]\n# ^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^ ^^^^^^^^^^^\n# Node's tag/name Node's type A fixed set of edges\n# Another possible The very first edge (in this example `x`) is considered\n# value is to be the output of the node\n# `Deterministic`","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"This expression registers a new node that can be used with the inference engine. Note howeve, that the @node macro does not generate any message passing update rules. These must be defined using the @rule macro.","category":"page"},{"location":"lib/nodes/nodes/#lib-node-types","page":"Overview","title":"Node types","text":"","category":"section"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"We distinguish different types of factor nodes in order to have better control over Bethe Free Energy computation. Each factor node has either the Deterministic or Stochastic functional form type.","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"Deterministic\nStochastic\nisdeterministic\nisstochastic\nsdtype","category":"page"},{"location":"lib/nodes/nodes/#ReactiveMP.Deterministic","page":"Overview","title":"ReactiveMP.Deterministic","text":"Deterministic\n\nDeterministic object used to parametrize factor node object with determinstic type of relationship between variables.\n\nSee also: Stochastic, isdeterministic, isstochastic, sdtype\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.Stochastic","page":"Overview","title":"ReactiveMP.Stochastic","text":"Stochastic\n\nStochastic object used to parametrize factor node object with stochastic type of relationship between variables.\n\nSee also: Deterministic, isdeterministic, isstochastic, sdtype\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.isdeterministic","page":"Overview","title":"ReactiveMP.isdeterministic","text":"isdeterministic(node)\n\nFunction used to check if factor node object is deterministic or not. Returns true or false.\n\nSee also: Deterministic, Stochastic, isstochastic, sdtype\n\n\n\n\n\n","category":"function"},{"location":"lib/nodes/nodes/#ReactiveMP.isstochastic","page":"Overview","title":"ReactiveMP.isstochastic","text":"isstochastic(node)\n\nFunction used to check if factor node object is stochastic or not. Returns true or false.\n\nSee also: Deterministic, Stochastic, isdeterministic, sdtype\n\n\n\n\n\n","category":"function"},{"location":"lib/nodes/nodes/#ReactiveMP.sdtype","page":"Overview","title":"ReactiveMP.sdtype","text":"sdtype(object)\n\nReturns either Deterministic or Stochastic for a given object (if defined).\n\nSee also: Deterministic, Stochastic, isdeterministic, isstochastic\n\n\n\n\n\n","category":"function"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"using ReactiveMP, BayesBase, Distributions, ExponentialFamily","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"For example the + node has the Deterministic type:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"plus_node = make_node(+)\n\nprintln(\"Is `+` node deterministic: \", isdeterministic(plus_node))\nprintln(\"Is `+` node stochastic: \", isstochastic(plus_node))\nnothing #hide","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"On the other hand, the Bernoulli node has the Stochastic type:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"bernoulli_node = make_node(Bernoulli)\n\nprintln(\"Is `Bernoulli` node deterministic: \", isdeterministic(bernoulli_node))\nprintln(\"Is `Bernoulli` node stochastic: \", isstochastic(bernoulli_node))","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"To get an actual instance of the type object we use sdtype function:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"println(\"sdtype() of `+` node is \", sdtype(plus_node))\nprintln(\"sdtype() of `Bernoulli` node is \", sdtype(bernoulli_node))\nnothing #hide","category":"page"},{"location":"lib/nodes/nodes/#lib-node-functional-dependencies-pipeline","page":"Overview","title":"Node functional dependencies pipeline","text":"","category":"section"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"The generic implementation of factor nodes in ReactiveMP supports custom functional dependency pipelines. Briefly, the functional dependencies pipeline defines what dependencies are need to compute a single message. As an example, consider the belief-propagation message update equation for a factor node f with three edges: x, y and z:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"mu(x) = int mu(y) mu(z) f(x y z) mathrmdy mathrmdz","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"Here we see that in the standard setting for the belief-propagation message out of edge x, we need only messages from the edges y and z. In contrast, consider the variational message update rule equation with mean-field assumption:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"mu(x) = exp int q(y) q(z) log f(x y z) mathrmdy mathrmdz","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"We see that in this setting, we do not need messages mu(y) and mu(z), but only the marginals q(y) and q(z). The purpose of a functional dependencies pipeline is to determine functional dependencies (a set of messages or marginals) that are needed to compute a single message. By default, ReactiveMP.jl uses so-called DefaultFunctionalDependencies that correctly implements belief-propagation and variational message passing schemes (including both mean-field and structured factorisations). The full list of built-in pipelines is presented below:","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"ReactiveMP.DefaultFunctionalDependencies\nReactiveMP.RequireMessageFunctionalDependencies\nReactiveMP.RequireMarginalFunctionalDependencies\nReactiveMP.RequireEverythingFunctionalDependencies","category":"page"},{"location":"lib/nodes/nodes/#ReactiveMP.DefaultFunctionalDependencies","page":"Overview","title":"ReactiveMP.DefaultFunctionalDependencies","text":"DefaultFunctionalDependencies\n\nThis pipeline translates directly to enforcing a variational message passing scheme. In order to compute a message out of some edge, this pipeline requires messages from edges within the same edge-cluster and marginals over other edge-clusters.\n\nSee also: ReactiveMP.RequireMessageFunctionalDependencies, ReactiveMP.RequireMarginalFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.RequireMessageFunctionalDependencies","page":"Overview","title":"ReactiveMP.RequireMessageFunctionalDependencies","text":"RequireMessageFunctionalDependencies(indices::Tuple, start_with::Tuple)\n\nThe same as DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the inbound message on the this edge.\n\nArguments\n\nindices::Tuple, tuple of integers, which indicates what edges should require inbound messages\nstart_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial inbound messages for edges in indices\n\nNote: start_with uses setmessage! mechanism, hence, it can be visible by other listeners on the same edge. Explicit call to setmessage! overwrites whatever has been passed in start_with.\n\n@model macro accepts a simplified construction of this pipeline:\n\n@model function some_model()\n # ...\n y ~ NormalMeanVariance(x, τ) where {\n pipeline = RequireMessage(x = vague(NormalMeanPrecision), τ)\n # ^^^ ^^^\n # request 'inbound' for 'x' we may do the same for 'τ',\n # and initialise with `vague(...)` but here we skip initialisation\n }\n # ...\nend\n\nDeprecation warning: RequireInboundFunctionalDependencies has been deprecated in favor of RequireMessageFunctionalDependencies.\n\nSee also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMarginalFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.RequireMarginalFunctionalDependencies","page":"Overview","title":"ReactiveMP.RequireMarginalFunctionalDependencies","text":"RequireMarginalFunctionalDependencies(indices::Tuple, start_with::Tuple)\n\nSimilar to DefaultFunctionalDependencies, but in order to compute a message out of some edge also requires the posterior marginal on that edge.\n\nArguments\n\nindices::Tuple, tuple of integers, which indicates what edges should require their own marginals\nstart_with::Tuple, tuple of nothing or <:Distribution, which specifies the initial marginal for edges in indices\n\nNote: start_with uses the setmarginal! mechanism, hence it can be visible to other listeners on the same edge. Explicit calls to setmarginal! overwrites whatever has been passed in start_with.\n\n@model macro accepts a simplified construction of this pipeline:\n\n@model function some_model()\n # ...\n y ~ NormalMeanVariance(x, τ) where {\n pipeline = RequireMarginal(x = vague(NormalMeanPrecision), τ)\n # ^^^ ^^^\n # request 'marginal' for 'x' we may do the same for 'τ',\n # and initialise with `vague(...)` but here we skip initialisation\n }\n # ...\nend\n\nNote: The simplified construction in @model macro syntax is only available in GraphPPL.jl of version >2.2.0.\n\nSee also: ReactiveMP.DefaultFunctionalDependencies, ReactiveMP.RequireMessageFunctionalDependencies, ReactiveMP.RequireEverythingFunctionalDependencies\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.RequireEverythingFunctionalDependencies","page":"Overview","title":"ReactiveMP.RequireEverythingFunctionalDependencies","text":"RequireEverythingFunctionalDependencies\n\nThis pipeline specifies that in order to compute a message of some edge update rules request everything that is available locally. This includes all inbound messages (including on the same edge) and marginals over all local edge-clusters (this may or may not include marginals on single edges, depends on the local factorisation constraint).\n\nSee also: DefaultFunctionalDependencies, RequireMessageFunctionalDependencies, RequireMarginalFunctionalDependencies\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#lib-node-traits","page":"Overview","title":"Node traits","text":"","category":"section"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"Each factor node has to define the ReactiveMP.as_node_functional_form trait function and to specify a ReactiveMP.ValidNodeFunctionalForm singleton as a return object. By default ReactiveMP.as_node_functional_form returns ReactiveMP.UndefinedNodeFunctionalForm. Objects that do not specify this property correctly cannot be used in model specification.","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"note: Note\n@node macro does that automatically","category":"page"},{"location":"lib/nodes/nodes/","page":"Overview","title":"Overview","text":"ReactiveMP.ValidNodeFunctionalForm\nReactiveMP.UndefinedNodeFunctionalForm\nReactiveMP.as_node_functional_form","category":"page"},{"location":"lib/nodes/nodes/#ReactiveMP.ValidNodeFunctionalForm","page":"Overview","title":"ReactiveMP.ValidNodeFunctionalForm","text":"ValidNodeFunctionalForm\n\nTrait specification for an object that can be used in model specification as a factor node.\n\nSee also: ReactiveMP.as_node_functional_form, ReactiveMP.UndefinedNodeFunctionalForm\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.UndefinedNodeFunctionalForm","page":"Overview","title":"ReactiveMP.UndefinedNodeFunctionalForm","text":"UndefinedNodeFunctionalForm\n\nTrait specification for an object that can not be used in model specification as a factor node.\n\nSee also: ReactiveMP.as_node_functional_form, ReactiveMP.ValidNodeFunctionalForm\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/nodes/#ReactiveMP.as_node_functional_form","page":"Overview","title":"ReactiveMP.as_node_functional_form","text":"as_node_functional_form(object)\n\nDetermines object node functional form trait specification. Returns either ValidNodeFunctionalForm() or UndefinedNodeFunctionalForm().\n\nSee also: ReactiveMP.ValidNodeFunctionalForm, ReactiveMP.UndefinedNodeFunctionalForm\n\n\n\n\n\n","category":"function"},{"location":"lib/nodes/flow/#lib-nodes-flow","page":"Flow","title":"Flow node","text":"","category":"section"},{"location":"lib/nodes/flow/","page":"Flow","title":"Flow","text":"See also Flow tutorial for a comprehensive guide on using flows in ReactiveMP.","category":"page"},{"location":"lib/nodes/flow/","page":"Flow","title":"Flow","text":"PlanarFlow\nRadialFlow\nFlowModel\nCompiledFlowModel\ncompile\nAdditiveCouplingLayer\nInputLayer\nPermutationLayer\nFlowMeta","category":"page"},{"location":"lib/nodes/flow/#ReactiveMP.PlanarFlow","page":"Flow","title":"ReactiveMP.PlanarFlow","text":"The PlanarFlow function is defined as\n\nf(bfx) = bfx + bfu tanh(bfw^top bfx + b)\n\nwith input and output dimension D. Here bfxin mathbbR^D represents the input of the function. Furthermore bfuin mathbbR^D, bfwin mathbbR^D and binmathbbR represent the parameters of the function. The function contracts and expands the input space. \n\nThis function has been introduced in:\n\nRezende, Danilo, and Shakir Mohamed. \"Variational inference with normalizing flows.\" International conference on machine learning. PMLR, 2015.\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.RadialFlow","page":"Flow","title":"ReactiveMP.RadialFlow","text":"The RadialFlow function is defined as\n\nf(bfx) = bfx + fracbeta(bfz - bfz_0)alpha + bfz - bfz_0\n\nwith input and output dimension D. Here bfxin mathbbR^D represents the input of the function. Furthermore bfz_0in mathbbR^D, alphain mathbbR and betainmathbbR represent the parameters of the function. The function contracts and expands the input space. \n\nThis function has been introduced in:\n\nRezende, Danilo, and Shakir Mohamed. \"Variational inference with normalizing flows.\" International conference on machine learning. PMLR, 2015.\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.FlowModel","page":"Flow","title":"ReactiveMP.FlowModel","text":"The FlowModel structure is the most generic type of Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimensionality and a tuple of layers and can be constructed as FlowModel( dim, (layer1, layer2, ...) ).\n\nNote: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.CompiledFlowModel","page":"Flow","title":"ReactiveMP.CompiledFlowModel","text":"The CompiledFlowModel structure is the most generic type of compiled Flow model, in which the layers are not constrained to be of a specific type. The FlowModel structure contains the input dimension and a tuple of compiled layers. Do not manually create a CompiledFlowModel! Instead create a FlowModel first and compile it with compile(model::FlowModel). This will make sure that all layers/mappings are configured with the proper dimensionality and with randomly sampled parameters. Alternatively, if you would like to pass your own parameters, call compile(model::FlowModel, params::Vector).\n\nNote: this model can be specialized by constraining the types of layers. This potentially allows for more efficient specialized methods that can deal with specifics of these layers, such as triangular jacobian matrices.\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.compile","page":"Flow","title":"ReactiveMP.compile","text":"compile() compiles a model by setting its parameters. It randomly sets parameter values in the layers and flows such that inference in the model can be obtained.\n\nInput arguments\n\nmodel::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.\n\nReturn arguments\n\n::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.\n\n\n\n\n\ncompile(model::FlowModel, params::Vector) lets you initialize a model model with a vector of parameters params.\n\nInput arguments\n\nmodel::FlowModel - a model of which the dimensionality of its layers/flows has been initialized, but its parameters have not been set.\nparams::Vector - a vector of parameters with which the model should be compiled.\n\nReturn arguments\n\n::CompiledFlowModel - a compiled model with set parameters, such that it can be used for processing data.\n\n\n\n\n\n","category":"function"},{"location":"lib/nodes/flow/#ReactiveMP.AdditiveCouplingLayer","page":"Flow","title":"ReactiveMP.AdditiveCouplingLayer","text":"The additive coupling layer specifies an invertible function bfy = g(bfx) following the specific structure (for the mapping g mathbbR^2 rightarrow mathbbR^2):\n\n beginalign\n y_1 = x_1 \n y_2 = x_2 + f(x_1)\n endalign\n\nwhere f(cdot) denotes an arbitrary function with mapping f mathbbR rightarrow mathbbR. This function can be chosen arbitrarily complex. Non-linear functions (neural networks) are often chosen to model complex relationships. From the definition of the model, invertibility can be easily achieved as\n\n beginalign\n x_1 = y_1 \n x_2 = y_2 - f(y_1)\n endalign\n\nThe current implementation only allows for the mapping g mathbbR^2 rightarrow mathbbR^2, although this layer can be generalized for arbitrary input dimensions.\n\nAdditiveCouplingLayer(f <: AbstractCouplingFlow) creates the layer structure with function f.\n\nExample\n\nf = PlanarFlow()\nlayer = AdditiveCouplingLayer(f)\n\nThis layer structure has been introduced in:\n\nDinh, Laurent, David Krueger, and Yoshua Bengio. \"Nice: Non-linear independent components estimation.\" arXiv preprint arXiv:1410.8516 (2014).\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.InputLayer","page":"Flow","title":"ReactiveMP.InputLayer","text":"The input layer specifies the input dimension to a flow model.\n\nlayer = InputLayer(3)\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.PermutationLayer","page":"Flow","title":"ReactiveMP.PermutationLayer","text":"The permutation layer specifies an invertible mapping bfy = g(bfx) = Pbfx where P is a permutation matrix.\n\n\n\n\n\n","category":"type"},{"location":"lib/nodes/flow/#ReactiveMP.FlowMeta","page":"Flow","title":"ReactiveMP.FlowMeta","text":"The FlowMeta structure contains the meta data of the Flow factor node. More specifically, it contains the model of the Flow factor node. The FlowMeta structure can be constructed as FlowMeta(model). Make sure that the flow model has been compiled.\n\nThe FlowMeta structure is required for the Flow factor node and can be included with the Flow node as: y ~ Flow(x) where { meta = FlowMeta(...) }\n\n\n\n\n\n","category":"type"},{"location":"extra/contributing/#Contribution-guidelines","page":"Contributing","title":"Contribution guidelines","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We welcome all possible contributors. This page details the some of the guidelines that should be followed when contributing to this package.","category":"page"},{"location":"extra/contributing/#Reporting-bugs","page":"Contributing","title":"Reporting bugs","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We track bugs using GitHub issues. We encourage you to write complete, specific, reproducible bug reports. Mention the versions of Julia and ReactiveMP for which you observe unexpected behavior. Please provide a concise description of the problem and complement it with code snippets, test cases, screenshots, tracebacks or any other information that you consider relevant. This will help us to replicate the problem and narrow the search space for solutions.","category":"page"},{"location":"extra/contributing/#Suggesting-features","page":"Contributing","title":"Suggesting features","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We welcome new feature proposals. However, before submitting a feature request, consider a few things:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"Does the feature require changes in the core ReactiveMP.jl code? If it doesn't (for example, you would like to add a factor node for a particular application), you can add local extensions in your script/notebook or consider making a separate repository for your extensions.\nIf you would like to add an implementation of a feature that changes a lot in the core ReactiveMP.jl code, please open an issue on GitHub and describe your proposal first. This will allow us to discuss your proposal with you before you invest your time in implementing something that may be difficult to merge later on.","category":"page"},{"location":"extra/contributing/#Contributing-code","page":"Contributing","title":"Contributing code","text":"","category":"section"},{"location":"extra/contributing/#Installing-ReactiveMP","page":"Contributing","title":"Installing ReactiveMP","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We suggest that you use the dev command from the new Julia package manager to install ReactiveMP.jl for development purposes. To work on your fork of ReactiveMP.jl, use your fork's URL address in the dev command, for example:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"] dev git@github.com:your_username/ReactiveMP.jl.git","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"The dev command clones ReactiveMP.jl to ~/.julia/dev/ReactiveMP. All local changes to ReactiveMP code will be reflected in imported code.","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"note: Note\nIt is also might be useful to install Revise.jl package as it allows you to modify code and use the changes without restarting Julia.","category":"page"},{"location":"extra/contributing/#Committing-code","page":"Contributing","title":"Committing code","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We use the standard GitHub Flow workflow where all contributions are added through pull requests. In order to contribute, first fork the repository, then commit your contributions to your fork, and then create a pull request on the master branch of the ReactiveMP.jl repository.","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"Before opening a pull request, please make sure that all tests pass without failing! All demos (can be found in /demo/ directory) and benchmarks (can be found in /benchmark/ directory) have to run without errors as well.","category":"page"},{"location":"extra/contributing/#Style-conventions","page":"Contributing","title":"Style conventions","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"note: Note\nReactiveMP.jl repository contains scripts to automatically format code according to our guidelines. Use make format command to fix code style. This command overwrites files.","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We use default Julia style guide. We list here a few important points and our modifications to the Julia style guide:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"Use 4 spaces for indentation\nType names use UpperCamelCase. For example: AbstractFactorNode, RandomVariable, etc..\nFunction names are lowercase with underscores, when necessary. For example: activate!, randomvar, as_variable, etc..\nVariable names and function arguments use snake_case\nThe name of a method that modifies its argument(s) must end in !","category":"page"},{"location":"extra/contributing/#Unit-tests","page":"Contributing","title":"Unit tests","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"We use the test-driven development (TDD) methodology for ReactiveMP.jl development. The test coverage should be as complete as possible. Please make sure that you write tests for each piece of code that you want to add.","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"All unit tests are located in the /test/ directory. The /test/ directory follows the structure of the /src/ directory. Each test file should have following filename format: test_*.jl. Some tests are also present in jldoctest docs annotations directly in the source code. See Julia's documentation about doctests.","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"The tests can be evaluated by running following command in the Julia REPL:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"] test ReactiveMP","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"In addition tests can be evaluated by running following command in the ReactiveMP root directory:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"make test","category":"page"},{"location":"extra/contributing/#Fixes-to-external-libraries","page":"Contributing","title":"Fixes to external libraries","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"If a bug has been discovered in an external dependencies of the ReactiveMP.jl it is the best to open an issue directly in the dependency's github repository. You use can use the fixes.jl file for hot-fixes before a new release of the broken dependecy is available.","category":"page"},{"location":"extra/contributing/#Makefile","page":"Contributing","title":"Makefile","text":"","category":"section"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"ReactiveMP.jl uses Makefile for most common operations:","category":"page"},{"location":"extra/contributing/","page":"Contributing","title":"Contributing","text":"make help: Shows help snippet\nmake test: Run tests, supports extra arguments\nmake test test_args=\"distributions:normal_mean_variance\" would run tests only from distributions/test_normal_mean_variance.jl\nmake test test_args=\"distributions:normal_mean_variance models:lgssm\" would run tests both from distributions/test_normal_mean_variance.jl and models/test_lgssm.jl\nmake docs: Compile documentation\nmake benchmark: Run simple benchmark\nmake lint: Check codestyle\nmake format: Check and fix codestyle ","category":"page"},{"location":"extra/extensions/#Extensions-and-interaction-with-the-Julia-ecosystem","page":"Extensions and interaction with the Julia ecosystem","title":"Extensions and interaction with the Julia ecosystem","text":"","category":"section"},{"location":"extra/extensions/","page":"Extensions and interaction with the Julia ecosystem","title":"Extensions and interaction with the Julia ecosystem","text":"ReactiveMP.jl exports extra functionality if other Julia packages are loaded in the same environment.","category":"page"},{"location":"extra/extensions/#Optimisers.jl","page":"Extensions and interaction with the Julia ecosystem","title":"Optimisers.jl","text":"","category":"section"},{"location":"extra/extensions/","page":"Extensions and interaction with the Julia ecosystem","title":"Extensions and interaction with the Julia ecosystem","text":"The Optimizers.jl package defines many standard gradient-based optimisation rules, and tools for applying them to deeply nested models. The optimizers defined in the Optimziers.jl are compatible with the CVI approximation method.","category":"page"},{"location":"extra/extensions/#DiffResults.jl-(loaded-automatically-with-the-ForwardDiff.jl)","page":"Extensions and interaction with the Julia ecosystem","title":"DiffResults.jl (loaded automatically with the ForwardDiff.jl)","text":"","category":"section"},{"location":"extra/extensions/","page":"Extensions and interaction with the Julia ecosystem","title":"Extensions and interaction with the Julia ecosystem","text":"The DiffResults.jl provides the DiffResult type, which can be passed to in-place differentiation methods instead of an output buffer. If loaded in the current Julia session enables faster derivatives with the ForwardDiffGrad option in the CVI approximation method (in the Gaussian case).","category":"page"},{"location":"custom/custom-functional-form/#custom-functional-form","page":"Custom functional form","title":"Custom Functional Form Specification","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"In a nutshell, functional form constraints defines a function that approximates the product of colliding messages and computes posterior marginal that can be used later on during the inference procedure. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. For example, if we refer to our CustomFunctionalForm as to f we can see the whole functional form constraints pipeline as:","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"q(x) = fleft(fracoverrightarrowmu(x)overleftarrowmu(x)int overrightarrowmu(x)overleftarrowmu(x) mathrmdxright)","category":"page"},{"location":"custom/custom-functional-form/#Interface","page":"Custom functional form","title":"Interface","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"ReactiveMP.jl, however, uses some extra utility functions to define functional form constraint behaviour. Here we briefly describe all utility function. If you are only interested in the concrete example, you may directly head to the Custom Functional Form example at the end of this section.","category":"page"},{"location":"custom/custom-functional-form/#Abstract-super-type","page":"Custom functional form","title":"Abstract super type","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"AbstractFormConstraint\nCompositeFormConstraint","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.AbstractFormConstraint","page":"Custom functional form","title":"ReactiveMP.AbstractFormConstraint","text":"AbstractFormConstraint\n\nEvery functional form constraint is a subtype of AbstractFormConstraint abstract type.\n\nNote: this is not strictly necessary, but it makes automatic dispatch easier and compatible with the CompositeFormConstraint.\n\nSee also: CompositeFormConstraint\n\n\n\n\n\n","category":"type"},{"location":"custom/custom-functional-form/#ReactiveMP.CompositeFormConstraint","page":"Custom functional form","title":"ReactiveMP.CompositeFormConstraint","text":"CompositeFormConstraint\n\nCreates a composite form constraint that applies form constraints in order. The composed form constraints must be compatible and have the exact same form_check_strategy. Any functional form constraint that defines is_point_mass_form_constraint() = true may be used only as the last element of the composition.\n\n\n\n\n\n","category":"type"},{"location":"custom/custom-functional-form/#Form-check-strategy","page":"Custom functional form","title":"Form check strategy","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"Every custom functional form must implement a new method for the default_form_check_strategy function that returns either FormConstraintCheckEach or FormConstraintCheckLast.","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"FormConstraintCheckLast: q(x) = f(μ1(x) * μ2(x) * μ3(x))\nFormConstraintCheckEach: q(x) = f(f(μ1(x) * μ2(x)) * μ3(x))","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"default_form_check_strategy\nFormConstraintCheckEach\nFormConstraintCheckLast","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.default_form_check_strategy","page":"Custom functional form","title":"ReactiveMP.default_form_check_strategy","text":"default_form_check_strategy(form_constraint)\n\nReturns a default check strategy (e.g. FormConstraintCheckEach or FormConstraintCheckEach) for a given form constraint object.\n\nSee also: FormConstraintCheckEach, FormConstraintCheckLast, constrain_form\n\n\n\n\n\n","category":"function"},{"location":"custom/custom-functional-form/#ReactiveMP.FormConstraintCheckEach","page":"Custom functional form","title":"ReactiveMP.FormConstraintCheckEach","text":"FormConstraintCheckEach\n\nThis form constraint check strategy checks functional form of the messages product after each product in an equality chain. Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed after each subsequent prod function.\n\nSee also: FormConstraintCheckLast, default_form_check_strategy, constrain_form\n\n\n\n\n\n","category":"type"},{"location":"custom/custom-functional-form/#ReactiveMP.FormConstraintCheckLast","page":"Custom functional form","title":"ReactiveMP.FormConstraintCheckLast","text":"FormConstraintCheckEach\n\nThis form constraint check strategy checks functional form of the last messages product in the equality chain. Usually if a variable has been connected to multiple nodes we want to perform multiple prod to obtain a posterior marginal. With this form check strategy constrain_form function will be executed only once after all subsequenct prod functions have been executed.\n\nSee also: FormConstraintCheckLast, default_form_check_strategy, constrain_form\n\n\n\n\n\n","category":"type"},{"location":"custom/custom-functional-form/#Prod-constraint","page":"Custom functional form","title":"Prod constraint","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"Every custom functional form must implement a new method for the default_prod_constraint function that returns a proper prod_constraint object.","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"default_prod_constraint","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.default_prod_constraint","page":"Custom functional form","title":"ReactiveMP.default_prod_constraint","text":"default_prod_constraint(form_constraint)\n\nReturns a default prod constraint needed to apply a given form_constraint. For most form constraints this function returns ProdGeneric.\n\n\n\n\n\n","category":"function"},{"location":"custom/custom-functional-form/#Constrain-form,-a.k.a-f","page":"Custom functional form","title":"Constrain form, a.k.a f","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"The main function that a custom functional form must implement, which we referred to as f in the beginning of this section, is the constrain_form function.","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"constrain_form","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.constrain_form","page":"Custom functional form","title":"ReactiveMP.constrain_form","text":"constrain_form(form_constraint, distribution)\n\nThis function must approximate distribution object in a form that satisfies form_constraint.\n\nSee also: FormConstraintCheckEach, FormConstraintCheckLast, default_form_check_strategy, is_point_mass_form_constraint\n\n\n\n\n\n","category":"function"},{"location":"custom/custom-functional-form/#Is-point-mass-form-constraint-(optional)","page":"Custom functional form","title":"Is point mass form constraint (optional)","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"Every custom functional form may implement a new method for the is_point_mass_form_constraint function that returns either true or false. This is an utility function that simplifes computation of the Bethe Free Energy and is not strictly necessary.","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"is_point_mass_form_constraint","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.is_point_mass_form_constraint","page":"Custom functional form","title":"ReactiveMP.is_point_mass_form_constraint","text":"is_point_mass_form_constraint(form_constraint)\n\nSpecifies whether form constraint always returns PointMass estimates or not. For a given form_constraint returns either true or false.\n\nSee also: FormConstraintCheckEach, FormConstraintCheckLast, constrain_form\n\n\n\n\n\n","category":"function"},{"location":"custom/custom-functional-form/#Compatibility-with-@constraints-macro-(optional)","page":"Custom functional form","title":"Compatibility with @constraints macro (optional)","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"To make custom functional form constraint compatible with the @constraints macro, it must implement a new method for the make_form_constraint function.","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"make_form_constraint","category":"page"},{"location":"custom/custom-functional-form/#ReactiveMP.make_form_constraint","page":"Custom functional form","title":"ReactiveMP.make_form_constraint","text":"make_form_constraint(::Type, args...; kwargs...)\n\nCreates form constraint object based on passed type with given args and kwargs. Used to simplify form constraint specification.\n\nAs an example:\n\nmake_form_constraint(PointMass)\n\ncreates an instance of PointMassFormConstraint and \n\nmake_form_constraint(SampleList, 5000, LeftProposal())\n\nshould create an instance of SampleListFormConstraint.\n\nSee also: AbstractFormConstraint\n\n\n\n\n\n","category":"function"},{"location":"custom/custom-functional-form/#custom-functional-form-example","page":"Custom functional form","title":"Custom Functional Form Example","text":"","category":"section"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"In this demo we show how to build a custom functional form constraint that is compatible with the ReactiveMP.jl inference backend. An important part of the functional forms constraint implementation is the prod function in the BayesBase package. We show a relatively simple use-case, which might not be very useful in practice, but serves as a simple step-by-step guide. Assume that we want a specific posterior marginal of some random variable in our model to have a specific Gaussian parametrisation, for example mean-precision. We can use built-in NormalMeanPrecision distribution, but we still need to define our custom functional form constraint:","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"using ReactiveMP, BayesBase\n\n# First we define our functional form structure with no fields\nstruct MeanPrecisionFormConstraint <: AbstractFormConstraint end","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"Next we define the behaviour of our functional form constraint:","category":"page"},{"location":"custom/custom-functional-form/","page":"Custom functional form","title":"Custom functional form","text":"ReactiveMP.is_point_mass_form_constraint(::MeanPrecisionFormConstraint) = false\nReactiveMP.default_form_check_strategy(::MeanPrecisionFormConstraint) = FormConstraintCheckLast()\nReactiveMP.default_prod_constraint(::MeanPrecisionFormConstraint) = GenericProd()\n\nfunction ReactiveMP.constrain_form(::MeanPrecisionFormConstraint, distribution) \n # This is quite a naive assumption, that a given `dsitribution` object has `mean` and `precision` defined\n # However this quantities might be approximated with some other external method, e.g. Laplace approximation\n m = mean(distribution) # or approximate with some other method\n p = precision(distribution) # or approximate with some other method\n return NormalMeanPrecision(m, p)\nend\n\nfunction ReactiveMP.constrain_form(::MeanPrecisionFormConstraint, distribution::BayesBase.ProductOf)\n # ProductOf is the special case, read about this type more in the corresponding documentation section\n # of the `BayesBase` package\n # ... \nend","category":"page"},{"location":"custom/custom-addons/#custom-addons","page":"Custom addons","title":"Custom Addons","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"Standard message passing schemes only pass along distributions to other nodes. However, for more advanced usage, there might be a need for passing along additional information in messages and/or marginals. One can for example think of passing along the scaling of the distribution or some information that specifies how the message or marginal was computed, i.e. which messages were used for its computation and which node was preceding it. Another use cases is saving extra debugging information inside messages themselves, e.g. what arguments have been used to compute a message.","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"Addons provide a solution here. Basically, addons are structures that contain extra information that are passed along the graph with messages and marginals in a tuple. These addons can be extracted using the getaddons(message/marginal) function. Its usage and operations can differ significantly for each application, yet below gives a concise overview on how to implement them on your own.","category":"page"},{"location":"custom/custom-addons/#Example","page":"Custom addons","title":"Example","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"Suppose that we wish to create an addon that counts the number of computations that preceded some message or marginal. This addon can be created by adding the file src/addons/count.jl and by including it in the ReactiveMP.jl file.","category":"page"},{"location":"custom/custom-addons/#Step-1:-Creating-the-addon-structure","page":"Custom addons","title":"Step 1: Creating the addon structure","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"Let's start by defining our new addon structure. This might seem daunting, but basically only requires us to specify the information that we would like to collect. Just make sure that it is specified as a subtype of AbstractAddon. In our example this becomes:","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"struct AddonCount{T} <: AbstractAddon\n count :: T\nend","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"You can add additional fields or functions for improved handling, such as get_count() or show() functions.","category":"page"},{"location":"custom/custom-addons/#Step-2:-Compute-addon-value-after-computing-a-message","page":"Custom addons","title":"Step 2: Compute addon value after computing a message","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"As a second step we need to specify how the addon behaves when a new message is computed in a factor node. For this purpose we need to implement a specialized version of the message_mapping_addon() function. This function accepts the mapping variables of the factor node and updates the addons by extending the tuple.","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"In our example we could write","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"# This specification assumes that the default value for addon is `AddonCount(nothing)`\nfunction message_mapping_addon(::AddonCount{Nothing}, mapping, messages, marginals, result, addons)\n\n # get number of operations of messages\n message_count = 0\n for message in messages\n message_count += getcount(message)\n end\n\n # get number of operations of marginals\n marginal_count = 0\n for marginal in marginals\n marginal_count += getcount(marginal)\n end\n\n # extend addons with AddonCount() structure\n return AddonCount(message_count + marginal_count + 1)\nend\n","category":"page"},{"location":"custom/custom-addons/#Step-3:-Computing-products","page":"Custom addons","title":"Step 3: Computing products","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"The goal is to update the AddonCount structure when we multiply 2 messages. As a result, we need to write a function that allows us to define this behaviour. This function is called multiply_addons and accepts 5 arguments. In our example this becomes","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"function multiply_addons(left_addon::AddonCount, right_addon::AddonCount, new_dist, left_dist, right_dist)\n return AddonCount(left_addon.count + right_addon.count + 1)\nend","category":"page"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"here we add the number of operations from the addons that are being multiplied and we add one (for the current operation). we are aware that this is likely not valid for iterative message passing schemes, but it still serves as a nice example. the left_addon and right_addon argument specify the addoncount objects that are being multiplied. corresponding to these addons, there are the distributions left_dist and right_dist, which might contain information for computing the product. the new distribution new_dist ∝ left_dist * right_dist is also passed along for potentially reusing the result of earlier computations.","category":"page"},{"location":"custom/custom-addons/#More-information","page":"Custom addons","title":"More information","text":"","category":"section"},{"location":"custom/custom-addons/","page":"Custom addons","title":"Custom addons","text":"For more advanced information check the implementation of the log-scale or memory addons.","category":"page"},{"location":"lib/methods/#lib-using-methods","page":"Exported methods","title":"Using methods from ReactiveMP","text":"","category":"section"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"In the Julia programming language (in contrast to Python for example) the most common way of loading a module is:","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"using ReactiveMP","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"A nice explanation about how modules/packages work in Julia can be found in the official documentation.","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"In a nutshell, Julia automatically resolves all name collisions and there is no a lot of benefit of importing specific names, e.g.:","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"import ReactiveMP: mean","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"One of the reasons for that is that Julia uses multiple-dispatch capabilities to merge names automatically and will indicate (with a warning) if something went wrong or names have unresolvable collisions on types. As a small example of this feature consider the following small import example:","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"import ReactiveMP: mean as mean_from_reactivemp\nimport Distributions: mean as mean_from_distributions\nimport BayesBase: mean as mean_from_bayes_base","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"mean_from_reactivemp === mean_from_distributions","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"mean_from_reactivemp === mean_from_bayes_base","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"Even though we import mean function from different packages they actually refer to the same object. Worth noting that this is not always the case - Julia will print a warning in case it finds unresolvable conflicts and usage of such functions will be disallowed unless user import them specifically. Read more about this in the section of the Julia's documentation.","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"# It is easier to let Julia resolve names automatically\n# Julia will not overwrite `mean` that is coming from both packages\nusing ReactiveMP, Distributions, BayesBase, ExponentialFamily","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"mean(Normal(0.0, 1.0)) # `Normal` is an object from `Distributions.jl`","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"mean(NormalMeanVariance(0.0, 1.0)) # `NormalMeanVariance` is an object from `ReactiveMP.jl`","category":"page"},{"location":"lib/methods/#lib-list-methods","page":"Exported methods","title":"List of available methods","text":"","category":"section"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"Below you can find a list of exported methods from ReactiveMP.jl. All methods (even private) can be always accessed with ReactiveMP. prefix, e.g ReactiveMP.mean.","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"note: Note\nSome exported names are (for legacy reasons) intended for private usage only. As a result some of these methods do not have a proper associated documentation with them. We constantly improve ReactiveMP.jl library and continue to add better documentation for many exported methods, but a small portion of these methods could be removed from this list in the future.","category":"page"},{"location":"lib/methods/","page":"Exported methods","title":"Exported methods","text":"using ReactiveMP #hide\nforeach(println, names(ReactiveMP))","category":"page"},{"location":"#ReactiveMP.jl","page":"Introduction","title":"ReactiveMP.jl","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"Julia package for reactive message passing Bayesian inference engine on a factor graph.","category":"page"},{"location":"","page":"Introduction","title":"Introduction","text":"note: Note\nThis package exports only an inference engine, for the full ecosystem with convenient model and constraints specification we refer user to the RxInfer.jl package and its documentation.","category":"page"},{"location":"#Examples-and-tutorials","page":"Introduction","title":"Examples and tutorials","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"Tutorials and examples are available in the RxInfer documentation.","category":"page"},{"location":"#Table-of-Contents","page":"Introduction","title":"Table of Contents","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"Pages = [\n \"lib/message.md\",\n \"lib/node.md\",\n \"lib/math.md\",\n \"extra/contributing.md\"\n]\nDepth = 2","category":"page"},{"location":"#Index","page":"Introduction","title":"Index","text":"","category":"section"},{"location":"","page":"Introduction","title":"Introduction","text":"","category":"page"},{"location":"lib/algebra/common/#lib-helpers-algebra-common","page":"Algebra utils","title":"Algebra common utilities","text":"","category":"section"},{"location":"lib/algebra/common/#lib-helpers-algebra-diageye","page":"Algebra utils","title":"diageye","text":"","category":"section"},{"location":"lib/algebra/common/","page":"Algebra utils","title":"Algebra utils","text":"diageye","category":"page"},{"location":"lib/algebra/common/#ReactiveMP.diageye","page":"Algebra utils","title":"ReactiveMP.diageye","text":"diageye(::Type{T}, n::Int)\n\nAn alias for the Matrix{T}(I, n, n). Returns a matrix of size n x n with ones (of type T) on the diagonal and zeros everywhere else.\n\n\n\n\n\ndiageye(n::Int)\n\nAn alias for the Matrix{Float64}(I, n, n). Returns a matrix of size n x n with ones (of type Float64) on the diagonal and zeros everywhere else.\n\n\n\n\n\n","category":"function"}] +} diff --git a/v3.13.3/siteinfo.js b/v3.13.3/siteinfo.js new file mode 100644 index 000000000..0ad6c49a4 --- /dev/null +++ b/v3.13.3/siteinfo.js @@ -0,0 +1 @@ +var DOCUMENTER_CURRENT_VERSION = "v3.13.3"; diff --git a/versions.js b/versions.js index 043c9551a..1735e9ca2 100644 --- a/versions.js +++ b/versions.js @@ -26,5 +26,5 @@ var DOC_VERSIONS = [ "v1.0", "dev", ]; -var DOCUMENTER_NEWEST = "v3.13.0"; +var DOCUMENTER_NEWEST = "v3.13.3"; var DOCUMENTER_STABLE = "stable";