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Implementation of domain-specific language (DSL) for dynamic probabilistic programming
Moved the images from OpenCat repo to reduce its size.
An open source quadruped robot pet framework for developing Boston Dynamics-style four-legged robots that are perfect for STEM, coding & robotics education, IoT robotics applications, AI-enhanced r…
DSL for probabilistic models specification and probabilistic programming.
Julia package for automated Bayesian inference on a factor graph with reactive message passing
Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.
Input Inference for Control (i2c), a control-as-inference framework for optimal control
List of Computer Science courses with video lectures.
Some Algorithms for Finite Element Modeling
Bayesian Python: Bayesian inference tools for Python
LIBLINEAR -- A Library for Large Linear Classification
Generate images from LaTeX equations that you can drag and drop, copy and paste or save to disk.
Haptic input knob with software-defined endstops and virtual detents
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Low level interface to control the Solo robots.
🎓 无需编写任何代码即可轻松创建漂亮的学术网站 Easily create a beautiful academic résumé or educational website using Hugo and GitHub. No code.
Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc.
A Python Package For System Identification Using NARMAX Models
An R library for (N)ARMAX black-box system identification
Python Kalman filtering and optimal estimation library. Implements Kalman filter, particle filter, Extended Kalman filter, Unscented Kalman filter, g-h (alpha-beta), least squares, H Infinity, smoo…
Fast, flexible and easy to use probabilistic modelling in Python.
High-performance reactive message-passing based Bayesian inference engine
The FactorGraph package provides the set of different functions to perform inference over the factor graph with continuous or discrete random variables using the belief propagation algorithm.