🧠 Implement Bayesian network inference in Python with exact and approximate algorithms for accurate probability estimation and performance analysis.

A domain specific language (DSL) for probabilistic graphical models

High-performance reactive message-passing based Bayesian inference engine

Bayesian inference with probabilistic programming.

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).

[AAAI 2026] The official implementation of the paper "BayesAgent: Bayesian Agentic Reasoning Under Uncertainty via Verbalized Probabilistic Graphical Modeling".

The JAGS Module

CompiledKnowledge is a Python package for compiling and querying discrete probabilistic graphical models.

Analyses, predictive models, and a monitoring dashboard to forecast future donation patterns, combining Bayesian models, Hidden Markov Models, and Generalized Linear Models.

PyHGF: A neural network library for predictive coding

Code for my PhD project on perceptual inference.

This repository contains a Sphinx project for the documentation of the Probabilistic Graphical Models course (master’s degree level), hosted on Read the Docs.

OpenBNSL is a unified framework for fair, reproducible, and transparent comparison of Bayesian Network Structure Learning (BNSL) algorithms.

Thermodynamic Hypergraphical Model Library in JAX

Experimental validation workspace for Extropic THRML: thermodynamic computing with JAX-accelerated block Gibbs sampling

Bayesian network structure learning using hybrid K2 search and hill climbing optimization. Discovers causal relationships in observational data across datasets with 8-50 variables and up to 10K samples.

A Python implementation of Bayesian Networks from scratch, featuring exact inference (Variable Elimination) and approximate inference algorithms (Rejection Sampling, Gibbs Sampling, and Likelihood Weighting).

This repo is a curated library to help you achieve a deeper understanding of what drives success and continuous improvement. Dive in, and discover content that can expand your thinking, sharpen your expertise, and fuel you drive better, whether you’re exploring new fields, honing in-demand skills, or simply looking for fresh perspectives.

Reimplementing a Graph Attention Network from Scratch in Python