Markov Chain Monte Carlo Python Packages

gpflow

Gaussian processes in TensorFlow

netket

Machine learning algorithms for many-body quantum systems

pymcmcstat

Python implementation of MATLAB toolbox "mcmcstat"

bisip

Bayesian inversion of SIP data

bayestorch

Lightweight Bayesian deep learning library for fast prototyping based on PyTorch

mici

MCMC samplers based on simulating Hamiltonian dynamics on a manifold.

hmclab

A numerical laboratory for Bayesian Seismology

dnest4

Diffusive Nested Sampling

lilit

Likelihood for LiteBIRD

littlemcmc

A lightweight and performant implementation of HMC and NUTS in Python, spun out of the PyMC project.

mcmc-samplers

A Python package for MCMC sampling.

mcmcallotter

This is a python package based on the source code for "An MCMC Based Course to Teaching Assistant Allocation", S. Kumar, S. Moothedath, P. Chaporkar, M. Belur, Proceedings of the Fifth International Conference on Network, Communication and Computing (2016). ACM.

occuspytial

A package for fast bayesian analysis of single season spatial occupancy models.

ls-mcmc

A light-weight library for large-scale Markov Chain Monte Carlo sampling

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.

gpflow-old

Gaussian process methods in tensorflow

lsmcmc

A light-weight library for large-scale Markov Chain Monte Carlo sampling