pymc-devs/pymc

Health v2 · maintenance only

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Adoption not part of the score

9723 stars · 2278 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

PyMC is a Python library for Bayesian statistical modeling and probabilistic programming, built on PyTensor. It provides intuitive model specification syntax with advanced MCMC samplers like NUTS and variational inference methods such as ADVI.

Use cases

• fit bayesian regression models in python
• run mcmc sampling for hierarchical models
• estimate posteriors with variational inference
• do probabilistic machine learning with uncertainty quantification
• impute missing values in a statistical model
• build custom probability distributions and models

When to choose

• you need full Bayesian inference with uncertainty estimates
• you want an intuitive syntax for specifying probabilistic models
• you need scalable samplers like NUTS for complex models
• you want variational inference for large datasets

When to avoid

• you only need simple frequentist statistics or scikit-learn-style point estimates
• you need extremely fast inference for huge datasets without approximation trade-offs
• you are not comfortable with Bayesian modeling concepts

Facets

library · maturity stable

machine-learning data-science math machine-learning data-science python cross-platform bayesian-inference mcmc variational-inference probabilistic-programming pytensor statistics algorithms

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