Sampling Methods Python Packages

blackjax

BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.

pydoe3

Design of experiments for Python

getdist

MCMC sample analysis, kernel densities, plotting, and GUI

bnlearn

Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.

zeus-mcmc

⚡️ zeus: Lightning Fast MCMC ⚡️

blackjax-nightly

BlackJAX is a Bayesian Inference library designed for ease of use, speed and modularity.

torchebm

🍓 Simulation-free, GPU-first generative modeling in PyTorch ⚡ Composable primitives for scalable, stable training of modern EBMs, diffusion, flow matching, and Schrödinger bridges.

dppy

Python toolbox for sampling Determinantal Point Processes

copulax

JAX-based copula modelling

pocomc

pocoMC: A Python implementation of Preconditioned Monte Carlo for accelerated Bayesian Computation

pyapr

Content-adaptive storage and processing of large volumetric microscopy data using the Adaptive Particle Representation (APR)

sgmcmcjax

Lightweight library of stochastic gradient MCMC algorithms written in JAX.

globaloptimizationhrla

A package to solve global optimization problems in small dimensions. The method is based on sampling algorithms, and specifically implements the high-resolution Langevin algorithm.

selene-sdk

a framework for training sequence-level deep learning networks

ontosample

OntoSample is a python package that offers classic sampling techniques for OWL ontologies/knowledge bases. Furthermore, we have tailored the classic sampling techniques to the setting of concept learning making use of learning problem.

outrank

A Python library for efficient feature ranking and selection on sparse data sets.

jaxampler

An open-source JAX-based statistical sampling toolkit 🧪

bajes

Bayesian Jenaer software

fldr

A near-optimal exact sampler for discrete probability distributions

active-sample

Implementation of active learning sampler

styne

Bayesian inference and sampling algorithms for high-dimensional problems and expensive forward models.

mcmc-samplers

A Python package for MCMC sampling.

sklearn-contrib-bde

Bayesian Deep Ensembles via MILE: easy to use, scikit-learn compatible and fast (JAX powered)

sampling-utils

Python tools to sample randomly with dont pick closest n elements constraints. Also contains a batch generator for the same to sample with replacement and with repeats if necessary.