Machine learning, in numpy

Bayesian Modeling and Probabilistic Programming in Python

Deep universal probabilistic programming with Python and PyTorch

A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow

Bayesian Convolutional Neural Network with Variational Inference based on Bayes by Backprop in PyTorch.

A Python library that helps data scientists to infer causation rather than observing correlation.

Probabilistic programming with NumPy powered by JAX for autograd and JIT compilation to GPU/TPU/CPU.

sbi is a Python package for simulation-based inference, designed to meet the needs of both researchers and practitioners. Whether you need fine-grained control or an easy-to-use interface, sbi has you covered.

Code for Bayesian Analysis

PyTensor allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.

BAyesian Model-Building Interface (Bambi) in Python.

Awesome resources on normalizing flows.

Collection of Monte Carlo (MC) and Markov Chain Monte Carlo (MCMC) algorithms applied on simple examples.

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

A unified framework for stochastic sampling packages and gravitational-wave inference in Python.

Dynamic Nested Sampling package for computing Bayesian posteriors and evidences

Sequential Monte Carlo in python

Collection of probabilistic models and inference algorithms

Gaussian processes in JAX and Flax.

ELFI - Engine for Likelihood-Free Inference