aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

Probabilistic reasoning and statistical analysis in TensorFlow

Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.

A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood.

Notebooks about Bayesian methods for machine learning

High-quality implementations of standard and SOTA methods on a variety of tasks.

A simple probabilistic programming language.

A collection of Bayesian data analysis recipes using PyMC3

rstanarm R package for Bayesian applied regression modeling

Data Assimilation with Python: a Package for Experimental Research

🐢 bayesAB: Fast Bayesian Methods for A/B Testing

🚂 Python API for Emma's Markov Model Algorithms 🚂

Implementation of robust dynamic Hamiltonian Monte Carlo methods (NUTS) in Julia.

Probabilistic Inference on Noisy Time Series

shinystan R package and ShinyStan GUI

Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)

A Python package for building Bayesian models with TensorFlow or PyTorch

Statistical Rethinking with PyTorch and Pyro

loo R package for approximate leave-one-out cross-validation (LOO-CV) and Pareto smoothed importance sampling (PSIS)