Gibbs Sampling Python Packages

aemcmc-nightly

Miscellaneous MCMC samplers written in Aesara

hamon

Hamon provides GPU-accelerated block Gibbs, adaptive non-reversible parallel tempering, and full round-trip diagnostics to drive scientific discovery in physics, probabilistic ML, and hard combinatorial problems.

smurff

Bayesian Factorization with Side Information in C++ with Python wrapper

pybbn

Learning and Inference in Bayesian Belief Networks

distributions

Primitives for Bayesian MCMC inference

aemcmc

Miscellaneous MCMC samplers written in Aesara

monte-library

monte-library is a set of Monte Carlo methods in Python. The package is written to be flexible, clear to understand and encompass variety of Monte Carlo methods.

ppgm

Graph: Representation, Learning, and Inference Methods