# pymc-devs/pymc

Bayesian Modeling and Probabilistic Programming in Python

Repository: https://github.com/pymc-devs/pymc
Canonical: https://ross.abutalabs.com/products/pymc
Homepage: https://www.pymc.io
Language: Python
License: NOASSERTION
License Family: other
Topics: python, statistical-analysis, bayesian-inference, mcmc, variational-inference, probabilistic-programming, pytensor
Last push: 2026-08-24T17:30:00+00:00

## Health v2 (maintenance only)
Score: 99/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 99, release rhythm 98, longevity 100
- inputs: {"age_days": 6329, "days_push": 9, "days_rel": 17, "gap_med": 15.5, "n_releases_24m": 33}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 9723, forks 2278 (observed 2026-08-28T04:10:36.967964+00:00)

## What it is
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
- artifact type: library
- maturity: stable
- function: machine-learning, data-science, math
- domain: machine-learning, data-science
- platform: python, cross-platform
- tags: bayesian-inference, mcmc, variational-inference, probabilistic-programming, pytensor, statistics, algorithms

## Member repositories
- pymc-devs/pymc (main) score 99

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:36.967964+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:21:13.908284+00:00, confidence not recorded.
  - readme: https://github.com/pymc-devs/pymc (fetched 2026-08-28T04:10:36.967964+00:00, sha 0af0e6c6332f)
  - homepage: https://www.pymc.io (fetched 2026-08-29T08:20:49.202896+00:00, sha 7b0381e4440b)
  - registry_pypi: https://pypi.org/pypi/pymc/json (fetched 2026-08-29T08:20:49.205529+00:00, sha 4f8b87675b5f)
- Data as of 2026-08-30T08:39:29.467469+00:00.