Bayesian Modeling and Probabilistic Programming in Python

Deep universal probabilistic programming with Python and PyTorch

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

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

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

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

BayesDB on SQLite. A Bayesian database table for querying the probable implications of data as easily as SQL databases query the data itself.

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

PyStan, the Python interface to Stan

Gaussian processes in JAX and Flax.

Lightwood is Legos for Machine Learning.

ProbLog is a Probabilistic Logic Programming Language for logic programs with probabilities.

Express & compile probabilistic programs for performant inference on CPU & GPU. Powered by JAX.

Oryx is a library for probabilistic programming and deep learning built on top of Jax.

Functional tensors for probabilistic programming

Probabilistic Programming and Nested sampling in JAX

Probabilistic programming framework that facilitates objective model selection for time-varying parameter models.

Probabilistic programming with large language models

Controlled text generation with programmable constraints

Probabilistic data structures in python http://pyprobables.readthedocs.io/en/latest/index.html