24 Open Source Bayesian Networks Software Projects

DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.

VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

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

Fast and Easy Infinite Neural Networks in Python

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

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

A web app to create and browse text visualizations for automated customer listening.

A Java Toolbox for Scalable Probabilistic Machine Learning

Bayesian Network Modeling and Analysis

Python tools for analyzing both classical and quantum Bayesian Networks

Software for learning sparse Bayesian networks

An implementation of Bayesian Networks Model for pure C++14 (11) later, including probability inference and structure learning method.

Implementation of Bayesian NNs in Pytorch (https://arxiv.org/pdf/1703.02910.pdf) (With some help from https://github.com/Riashat/Deep-Bayesian-Active-Learning/))

Risk Network Modeling and Analysis

R Wrapper for Tetrad Library

Python 3.7 version of David Barber's MATLAB BRMLtoolbox

COBAYN: Compiler Autotuning Framework Using Bayesian Networks

Repository of a data modeling and analysis tool based on Bayesian networks

R package for inference in Bayesian networks.

The junction tree algorithm for (discrete) factor graphs

🎲 A Kotlin DSL for probabilistic programming.

Gaussian dynamic Bayesian networks structure learning and inference based on the bnlearn package

Library for graphical models of decision making, based on pgmpy and networkx

Benchpress is a snakemake workflow for reproducible and scalable execution and benchmarks of structure learning algorithms for probabilistic graphical models.