A tool-box for prior elicitation#

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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.

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.


PreliZ is tested on Python 3.10+. And depends on ArviZ, matplotlib, NumPy, and SciPy. See [pyproject.toml](arviz-devs/preliz) for version information.


For the latest release (base set of dependencies) you can do:

pip install preliz

To make use of the interactive features, you can install the optional dependencies:

pip install "preliz[full,lab]"

The latest development version can be installed from the main branch using pip:

pip install git+https://github.com/arviz-devs/preliz.git


If you find PreliZ useful in your work, we kindly request that you cite the following paper:

author = {Icazatti, Alejandro and Abril-Pla, Oriol and Klami, Arto and Martin, Osvaldo A},
doi = {10.21105/joss.05499},
journal = {Journal of Open Source Software},
month = sep,
number = {89},
pages = {5499},
title = {{PreliZ: A tool-box for prior elicitation}},
url = {https://joss.theoj.org/papers/10.21105/joss.05499},
volume = {8},
year = {2023}


We welcome contributions from interested individuals or groups! For information about contributing to PreliZ check out our instructions, policies, and guidelines here.


See the GitHub contributor page.