Exploring and eliciting probability distributions
===================================================

|PyPI version|
|Tests|
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|Ruff|
|DOI|

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.. |Coverage| image:: https://codecov.io/gh/arviz-devs/preliz/branch/main/graph/badge.svg?token=SLJIK2O4C5 
    :target: https://codecov.io/gh/arviz-devs/preliz

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    :target: https://github.com/astral-sh/ruff

.. |DOI| image:: https://joss.theoj.org/papers/10.21105/joss.05499/status.svg
   :target: https://doi.org/10.21105/joss.05499


Overview
========

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.
* Separate tasks between humans and computers, so users can retain control of important decisions while numerically demanding, error-prone or tedious tasks are automatized.
* Prevent users to become overconfident in their own opinions.
* Easily integrate with other tools.
* Allow predictive elicitation.
* Having a simple and intuitive interface suitable for non-specialists in order to minimize cognitive biases and heuristics.
* Switching between different types of visualization such as kernel density estimates plots, quantile dotplots, histograms, etc. 
* Being agnostic of the underlying probabilistic programming language.
* Being modular.

.. toctree::
   :maxdepth: 2
   :hidden:
   :caption: Getting Started

   Overview <self>
   installation
   api_reference

.. toctree::
   :maxdepth: 2
   :hidden:
   :caption: Examples

   gallery_content
   gallery_examples

.. toctree::
   :maxdepth: 1
   :hidden:
   :caption: References

   citing
   contributing
   changelog

Donations
---------

PreliZ, as other ArviZ-devs projects, is a non-profit project under the NumFOCUS umbrella. If you want to support PreliZ financially, you can donate `here <https://numfocus.org/donate-to-arviz>`_.