PyMC is a Bayesian probabilistic programming framework used for building probabilistic models and performing Bayesian inference. It provides a probabilistic graphical model library for specifying random variables, priors, and likelihood functions, supported by an MCMC sampling engine and variational inference tools to estimate posterior distributions.
The framework features a GPU-accelerated inference backend that compiles models into machine code to increase execution speed. It utilizes a backend-agnostic tensor execution model and just-in-time graph compilation to optimize the computation of log-probabilities and gradients.
The project covers a wide range of statistical modeling capabilities, including Gaussian processes, survival analysis, causal inference, and time series forecasting. It supports the construction of generalized linear models, mixture models, and the integration of ordinary differential equations within probabilistic workflows.
The system includes tools for model convergence diagnosis and posterior distribution analysis to evaluate inference quality and model fit.
