PyMC
PyMC provides Bayesian modeling and probabilistic programming tools for Python.
About PyMC
PyMC is a Python package for Bayesian statistical modeling, focused on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms. Formerly known as PyMC3, it is designed for flexibility and extensibility across a large suite of problems.
Models are specified with a syntax that maps statistical notation to Python code, for example x ~ N(0,1) becomes x = Normal('x',0,1). Sampling is handled by algorithms such as the No U-Turn Sampler, and variational inference includes ADVI and mini-batch ADVI for large data sets. The package relies on PyTensor for computation optimization, dynamic C or JAX compilation, NumPy-style broadcasting and indexing, linear algebra operators, and support for missing value imputation. The source lists no pricing information.
Key features
- Model specification syntax such as x ~ N(0,1) translating to Python code
- Sampling algorithms including the No U-Turn Sampler for complex models
- Handles models with thousands of parameters using standard fitting algorithms
- Variational inference with ADVI and mini-batch ADVI for large data sets
- PyTensor backend enabling computation optimization and dynamic C or JAX compilation
- Transparent support for missing value imputation
Best for: Python users such as statisticians, researchers, and data scientists who need Bayesian modeling and probabilistic programming.
Good for
- Sampling algorithms allow complex models with thousands of parameters without deep knowledge of fitting algorithms
- Flexible and extensible, making it applicable to a large suite of problems
- Mini-batch ADVI supports analysis of large data sets
Watch out for
- It is a Python package, so use requires working in a Python environment
- No pricing details are provided in the source
PyMC at a glance
- Website
- github.com/pymc-devs/pymc
- Category
- Development › Developer Tools
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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PyMC FAQ
What algorithms does PyMC use?
It focuses on advanced Markov chain Monte Carlo (MCMC) and variational inference (VI) algorithms, including the No U-Turn Sampler and ADVI.
Does PyMC handle missing data?
Yes, it provides transparent support for missing value imputation.
Where can questions about PyMC be asked?
The project points users to the PyMC Discourse forum.
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