Exploring and eliciting probability distributions
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.
This repository hosts the PreliZ apps, a collection of interactive web applications built using Streamlit. These apps are a subset of the tools available in the PreliZ Python library, and are designed to provide an intuitive interface for users to explore and elicit probability distributions without needing to install the library or write code. The apps are hosted on Streamlit Cloud and can be accessed from any web browser.
If you find PreliZ useful in your work, we kindly request that you cite the following paper:
@article{Icazatti_2023,
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}
}
PreliZ is a community project and welcomes contributions. Additional information can be found in the Contributing Readme
PreliZ wishes to maintain a positive community. Additional details can be found in the Code of Conduct
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.
