{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/5"},{"label":"Mathematics","url":"https://skillfed.io/packages/category/scientific-engineering-mathematics/2"}],"enrichment":{"capability":"PyMC3 is a Python package for Bayesian statistical modeling and probabilistic machine learning, implementing advanced Markov chain Monte Carlo (MCMC) and variational inference algorithms for complex probabilistic models.","skillfed_tags":["bayesian-inference","probabilistic-programming","mcmc-sampling"],"use_cases":["Fit hierarchical Bayesian models to experimental or observational data with uncertainty quantification","Perform approximate Bayesian inference on large datasets using mini-batch ADVI","Build custom probabilistic models for time-series forecasting, causal inference, or mixed-effects analysis","Impute missing values in datasets while propagating uncertainty through downstream analyses","Compare competing statistical models using posterior predictive checks and model diagnostics"],"what_it_does":"PyMC3 is a mature probabilistic programming framework that lets you specify Bayesian statistical models using intuitive syntax and then fit them using state-of-the-art sampling and inference algorithms. It wraps theano-pymc for computational optimization and provides both MCMC methods (including the No U-Turn Sampler) and variational inference (ADVI) for approximate posterior estimation, making it applicable to a wide range of statistical modeling problems.\n\nThe package is designed for researchers and practitioners who need flexible Bayesian modeling without deep expertise in sampling algorithms. It handles missing value imputation transparently and supports complex models. However, PyMC3 is now considered legacy\u2014the project has been renamed to PyMC and moved to newer computational backends. New projects should evaluate whether to use the current PyMC version instead, though PyMC3 remains functional and documented.","worth_installing":"Yes, but with conditions. PyMC3 is production-stable and well-documented, with low install friction and no known vulnerabilities. However, it is now legacy\u2014the PyMC project has been renamed and moved to newer backends. Install PyMC3 if you are maintaining existing code, learning Bayesian modeling from established tutorials, or need its specific theano-pymc integration. For new projects, evaluate the current PyMC version first to avoid future maintenance burden."},"id":"pymc3","links":{"html":"https://skillfed.io/packages/pymc3","md":"https://skillfed.io/packages/pymc3.md","pypi":"https://pypi.org/project/pymc3/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2024-05-31","license_spdx":null,"license_treatment":"permissive","name":"pymc3","python_support":"supports_current","summary":"Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano"},"popularity":{"monthly_downloads":319118,"position":7647,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.11.6"}