Gibbs Sampling Python Packages

aemcmc-nightly

AeMCMC is a Python library that automates the construction of samplers for Aesara graphs representing statistical models.

ferrotherm

Thermodynamic computing in pure Rust: energy-based models, block-Gibbs, parallel tempering, thermodynamic linear algebra, stochastic differentiable programs, a variational compiler onto device topologies, and a joules ledger. Zero deps, wasm-clean, verified against exact physics — and it reaches real Ising silicon through the same trait as a CPU.

multisensor-lmb-filters-rs

50-1000x Faster multi-sensor labelled Bernoulli filters in Rust and Python via Py03 - A (mostly automated) port of https://github.com/scjrobertson/multisensor-lmb-filters

pybbn

Inference in Bayesian Belief Networks using Probability Propagation in Trees of Clusters (PPTC) and Gibbs sampling

smurff

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

distributions

Low-level primitives for collapsed Gibbs sampling in python and C++

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.

aemcmc

AeMCMC is a Python library that automates the construction of samplers for Aesara graphs representing statistical models.

enchilada

Blocked-Gibbs global-fit orchestration for LISA: the Wheel/Block/Residuals protocol

ppgm

Graph: Representation, Learning, and Inference Methods

monte-library

Monte 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.