Add reference to PriorDB (#535)

README.md

@@ -13,6 +13,8 @@ A tool-box for prior elicitation.
 
 Prior elicitation refers to the process of transforming the knowledge of a particular domain into well-defined probability distributions. Specifying useful priors is a central aspect of Bayesian statistics. PreliZ is a Python package aimed at helping practitioners choose prior distributions by offering a set of tools for the various facets of prior elicitation. It covers a range of methods, from unidimensional prior elicitation on the parameter space to predictive elicitation on the observed space. The goal is to be compatible with probabilistic programming languages (PPL) in the Python ecosystem like PyMC and PyStan, while remaining agnostic of any specific PPL.
 
+A good companion for PreliZ is [PriorDB](https://n-kall.github.io/priorDB/), a database of prior distributions for Bayesian analysis. It is a community-driven project that aims to provide a comprehensive collection of prior distributions for a wide range of models and applications. 
+
 ###  The Zen of PreliZ
 * Being open source, community-driven, diverse and inclusive.
 * Avoid fully-automated solutions, keep the human in the loop.

docs/index.rst

@@ -32,6 +32,8 @@ distributions by offering a set of tools for the various facets of prior elicita
 elicitation on the parameter space to predictive elicitation on the observed space. The goal is to be compatible with probabilistic programming
 languages (PPL) in the Python ecosystem like PyMC and PyStan, while remaining agnostic of any specific PPL.
 
+A good companion for PreliZ is `PriorDB <https://n-kall.github.io/priorDB/>`_, a database of prior distributions for Bayesian analysis. 
+It is a community-driven project that aims to provide a comprehensive collection of prior distributions for a wide range of models and applications. 
 
 The Zen of PreliZ
 -----------------
@@ -50,7 +52,8 @@ The Zen of PreliZ
 
 Dependencies
 ============
-PreliZ is tested on Python 3.10+. And depends on ArviZ, matplotlib, NumPy, and SciPy. See [pyproject.toml](https://github.com/arviz-devs/preliz/blob/main/pyproject.toml) for version information.
+PreliZ is tested on Python 3.10+. And depends on ArviZ, matplotlib, NumPy, and SciPy. See `pyproject.toml <https://github.com/arviz-devs/preliz/blob/main/pyproject.toml>`_ for version information.
+
 
 
 Installation