An editor has nominated this article for deletion. You are welcome to participate in the deletion discussion, which will decide whether or not to retain it. |
This article is an orphan, as no other articles link to it. Please introduce links to this page from related articles; try the Find link tool for suggestions. (November 2024) |
PreliZ is a Python package for exploring and eliciting probability distributions. While it is primarily focused on prior elicitation—the process of converting domain-specific knowledge into well-defined probability distributions—it can also be used to analyze distributions outside the context of Bayesian statistics.[1][2][3][4]
| Original author(s) | ArviZ Development Team |
|---|---|
| Initial release | September 21, 2023 |
| Repository | github |
| Written in | Python |
| Operating system | Unix-like, macOS, Windows |
| Platform | Intel x86 – 32-bit, x64 |
| Type | Statistical package |
| License | Apache License, Version 2.0 |
| Website | preliz |
PreliZ is an open source project developed by the community and it is part of the ArviZ family of packages.
Etymology
editPreliZ is a word play, relating Prior elicitation with the iZ particle to make the connection with its sister project ArviZ.
Library features
editPreliZ provides diverse features for exploring probability distributions and elicit priors [5][6].
- A wide array of probability distributions with associated methods including PDF, CDF, PPF, random sampling, moments, Credible interval (highest density and equally-tailed intervals) etc.
- Many distributions support more than one parameterization.
- Easy visualisation with KDEs, histograms, ecdf.
- Methods for unidimentional elicitation, like, roulette, maximum entropy, quartiles, etc.
- Methods for predictive elictitation.
- Interactive and graphical methods.
- Interface with PyMC, Bambi and potentially other PPLs.
References
edit- ^ Icazatti, Alejandro; Abril-Pla, Oriol; Klami, Arto; Martin, Osvaldo A. (2023). "PreliZ: A tool-box for prior elicitation". Journal of Open Source Software. 8 (89): 5499. doi:10.21105/joss.05499.
- ^ Zivich, Paul N.; Edwards, Jessie K.; Shook-Sa, Bonnie E.; Lofgren, Eric T.; Lessler, Justin; Cole, Stephen R. (2024). "Synthesis estimators for positivity violations with a continuous covariate". Journal of the Royal Statistical Society Series A: Statistics in Society. arXiv:2311.09388.
- ^ Mikkola, Petrus; Martin, Osvaldo A.; Chandramouli, Suyog; Hartmann, Marcelo; Abril Pla, Oriol; Thomas, Owen; Pesonen, Henri; Corander, Jukka; Vehtari, Aki; Kaski, Samuel; Bürkner, Paul-Christian; Klami, Arto (2024). "Prior Knowledge Elicitation: The Past, Present, and Future". Bayesian Analysis. 19 (4). International Society for Bayesian Analysis: 1129–1161. arXiv:2112.01380. doi:10.1214/23-BA1381.
- ^ Martin, Osvaldo (2024). Bayesian Analysis with Python - Third Edition: A practical guide to probabilistic modeling. Packt Publishing Ltd. ISBN 9781805127161.
- ^ Icazatti, Alejandro; Abril-Pla, Oriol; Klami, Arto; Martin, Osvaldo A. (2023). "PreliZ: A tool-box for prior elicitation". Journal of Open Source Software. 8 (89): 5499. doi:10.21105/joss.05499.
- ^ Icazatti, Alejandro; Abril-Pla, Oriol; Klami, Arto; Martin, Osvaldo A. (2024). "PreliZ: A tool-box for prior elicitation". Zenodo. doi:10.5281/zenodo.13991977.
External links
edit- Official website
- PriorDB a collaborative database of models and their priors