Home#
PyMC is a probabilistic programming library for Python that allows users to build Bayesian models with a simple Python API and fit them using Markov chain Monte Carlo (MCMC) methods.
Features#
PyMC strives to make Bayesian modeling as simple and painless as possible, allowing users to focus on their problem rather than the methods.
Here is what sets it apart:
Modern: Includes state-of-the-art inference algorithms, including MCMC (NUTS) and variational inference (ADVI).
User friendly: Write your models using friendly Python syntax. Learn Bayesian modeling from the many example notebooks.
Fast: Uses PyTensor as its computational backend to compile through C, Numba or JAX, run your models on the GPU, and benefit from complex graph-optimizations.
Batteries included: Includes probability distributions, Gaussian processes, ABC, SMC and much more. It integrates nicely with ArviZ for visualizations and diagnostics, as well as Bambi for high-level mixed-effect models.
Community focused: Ask questions on discourse, join MeetUp events, follow us on Twitter, and start contributing.
