{"categories":[{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"PreliZ helps you specify prior distributions for Bayesian models by providing interactive tools for prior elicitation, from parameter-space methods to predictive elicitation, designed to work with probabilistic programming languages like PyMC.","skillfed_tags":["bayesian-inference","prior-elicitation","interactive-visualization"],"use_cases":["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."],"what_it_does":"PreliZ is a toolkit for prior elicitation in Bayesian statistics\u2014the 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.\n\nThe 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.","worth_installing":"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."},"id":"preliz","links":{"html":"https://skillfed.io/packages/preliz","md":"https://skillfed.io/packages/preliz.md","pypi":"https://pypi.org/project/preliz/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-07","license_spdx":null,"license_treatment":"permissive","name":"preliz","python_support":"supports_current","summary":"Exploring and eliciting probability distributions."},"popularity":{"monthly_downloads":397479,"position":6959,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.27.1"}