Bayesian inference with probabilistic programming.

Python Library for learning (Structure and Parameter), inference (Probabilistic and Causal), and simulations in Bayesian Networks.

Implementation of various inference and learning algorithms for Probabilistic Graphical Models (PGMs) without off-the-shelf libraries. Also includes projects from the PGM specialization on Coursera offered by Stanford.

PyHGF: A neural network library for predictive coding

This repository is a mirror. If you want to raise an issue or contact us, we encourage you to do it on Gitlab (https://gitlab.com/agrumery/aGrUM).

Fast, flexible and easy to use probabilistic modelling in Python.

High-performance reactive message-passing based Bayesian inference engine

The JAGS Module

🚶Python Library for Random Walks

causact: R package to accelerate computational Bayesian inference workflows in R through interactive visualization of models and their output.

Variational autoencoder implemented in tensorflow and pytorch (including inverse autoregressive flow)

Type stable implementation of a Bayesian network.

⚗️ A curated list of Books, Research Papers, and Software for Bayesian Networks.

Blang's software development kit

Inference of microbial interaction networks from large-scale heterogeneous abundance data

Source code for the paper "Efficient Detection of Exchangeable Factors in Factor Graphs" (FLAIRS 2024)

High Performance Structured Prediction in PyTorch

Source code for the paper "Lifting Factor Graphs with Some Unknown Factors" (ECSQARU 2023)

Source code for the paper "Lifted Causal Inference in Relational Domains" (CLeaR 2024)

Source code for the paper "Colour Passing Revisited: Lifted Model Construction with Commutative Factors" (AAAI 2024)