VIP cheatsheets for Stanford's CS 221 Artificial Intelligence

Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) 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

Dynamic Bayesian Network library

Probability distributions in Clojure

The junction tree algorithm for (discrete) factor graphs

The source code repository for the FactorBase system

R package for inference in Bayesian networks.

Self Driven Vehicle using AI in Robotics ,i.e., Kalman filters, A* algorithm, PID control, localization, etc.The basic functionality of this car is just to chase and catch the running away car just like cops. For this, the car is such designed that is takes all the desired steps on its own in order to catch the running away car safely on a high traffic lane.

Distributed Training of Bayesian Neural Networks at Scale

bnviewer - An R package for Interactive Visualization of Bayesian Networks

Structure learning for Bayesian networks using the CCDr algorithm.

Source code for the paper "Causal Modeling of Twitter Activity During COVID-19" (2020)

Learning Bayesian Network parameters using Expectation-Maximisation

CS undergraduate thesis on uniform generation of k-trees for learning the structure of Bayesian networks (IME-USP 2016).

Github page: