PyMC is a Python package for Bayesian statistical modeling and probabilistic programming.
It’s designed for building expressive statistical models with an intuitive syntax, then fitting them using modern inference algorithms including advanced Markov chain Monte Carlo and variational inference methods. The project is aimed at statisticians, data scientists, researchers, and developers who need flexible tools for uncertainty quantification, hierarchical modeling, posterior analysis, and simulation-based workflows.
This is free and open source software.
Key Features
- Offers an intuitive Python syntax for defining Bayesian models.
- Supports advanced Markov chain Monte Carlo sampling, including the No-U-Turn Sampler.
- Includes variational inference methods for approximate posterior estimation.
- Uses PyTensor for computation optimization, compilation, broadcasting, indexing, and linear algebra operations.
- Provides transparent support for missing value imputation.
- Includes tools for prior predictive sampling, posterior predictive sampling, and model-based simulation.
Website: github.com/pymc-devs/pymc
Support:
Developer: PyMC Developers
License: Apache License 2.0
PyMC is written in Python. Learn Python with our recommended free books and free tutorials.
Related Software
| Python Mathematics Tools | |
|---|---|
| scikit-learn | Machine learning library for Python |
| NumPy | Core package for scientific computing with Python |
| SciPy | Ecosystem for mathematics, science, and engineering. |
| statsmodels | Statistical modeling and econometrics |
| JAX | Python library for high-performance numerical computing |
| SageMath | Computer algebra system |
| SymPy | Library for symbolic mathematics |
| PyMC | Bayesian statistical modeling and probabilistic programming |
| Pyomo | Object-oriented algebraic modeling language |
| patsy | Package for describing statistical models and to build design matrices |
| mpmath | Library for arbitrary-precision floating-point arithmetic |
| SfePy | Finite element software package |
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