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.
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).
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.