preliz
Exploring and eliciting probability distributions.
Decision gist · record as of 2026-08-14
Yes, if you are actively doing Bayesian modeling and want structured guidance on prior specification. The low install friction, active maintenance, permissive license, and focus on human-centered elicitation make it a solid choice for practitioners. The Alpha status warrants checking that your probabilistic programming language of choice is well-supported, but the package has no known security vulnerabilities and receives regular updates.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.12 or later.
- Interactive visualization features require optional dependencies (jupyterlab or jupyter notebook).
- Low friction: pure Python wheel, six common scientific dependencies (numpy, scipy, matplotlib, numba, arviz_stats, pytensor_distributions).
License · maintenance · safety
permissive license (permissive) — Permissive license allows commercial and private use with minimal restrictions; you may use, modify, and distribute the package freely provided you include the license notice.
last release 2026-07-07 (38 days) · last repo commit 2026-08-13 · 172 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 397,479 downloads/mo, #6,959 on PyPI
Alternatives
Verify before relying
pip install preliz
import preliz as pz
# Create and visualize a prior distribution
prior = pz.Normal(mu=0, sigma=1)
pz.plot_prior(prior)- Whether the package's integration with PyMC and PyStan is production-ready or still experimental given its Alpha status.
- Performance characteristics when working with high-dimensional prior elicitation tasks.
- Whether arviz_stats and pytensor_distributions are stable, maintained dependencies.
What it is and what it does
PreliZ is a toolkit for prior elicitation in Bayesian statistics—the process of translating domain knowledge into well-defined probability distributions. It provides interactive, human-centered tools for specifying priors across multiple methods: direct parameter-space elicitation, predictive elicitation on observed data, and visualization in multiple formats (kernel density estimates, quantile dotplots, histograms). The package is designed to remain agnostic of the underlying probabilistic programming language, making it a companion tool for frameworks in the Python ecosystem.
The package depends on numpy, scipy, matplotlib, numba, arviz_stats, and pytensor_distributions for numerical computation and visualization. It emphasizes keeping humans in the loop rather than fully automating prior selection, helping practitioners avoid overconfidence in their own assumptions while automating tedious or error-prone numerical tasks. It is currently in Alpha status and actively maintained.
Use it for
- Interactively specify and visualize prior distributions for Bayesian regression or classification models before fitting.
- Conduct predictive elicitation by examining how different priors affect predictions on your observed data.
- Explore and compare multiple prior specifications using quantile dotplots or kernel density estimates.
- Integrate prior elicitation workflows into probabilistic programming pipelines without being locked into a single language.
- Build domain-informed priors by transforming expert knowledge into probability distributions via guided tools.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are actively doing Bayesian modeling and want structured guidance on prior specification.
The low install friction, active maintenance, permissive license, and focus on human-centered elicitation make it a solid choice for practitioners. The Alpha status warrants checking that your probabilistic programming language of choice is well-supported, but the package has no known security vulnerabilities and receives regular updates.
Install
preliz on PyPI
Before you install
Low friction: pure Python wheel, six common scientific dependencies (numpy, scipy, matplotlib, numba, arviz_stats, pytensor_distributions). Active maintenance with a release 38 days ago; repository is not archived and has recent commits.
Requires Python 3.12 or later. Interactive visualization features require optional dependencies (jupyterlab or jupyter notebook).
License in practice
Permissive license allows commercial and private use with minimal restrictions; you may use, modify, and distribute the package freely provided you include the license notice.
Quickstart
pip install preliz
import preliz as pz
# Create and visualize a prior distribution
prior = pz.Normal(mu=0, sigma=1)
pz.plot_prior(prior)Verify before relying
- Whether the package's integration with PyMC and PyStan is production-ready or still experimental given its Alpha status.
- Performance characteristics when working with high-dimensional prior elicitation tasks.
- Whether arviz_stats and pytensor_distributions are stable, maintained dependencies.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.12 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 6 packagesarviz_statsmatplotlibnumbanumpypytensor_distributionsscipy |
| Maintenance | Actively maintained 38 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 397,479 / month, #6,959 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 3 - AlphaIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: preliz-0.27.1-py3-none-any.whl
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