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.

Python Library for Causal and Probabilistic Modeling using Bayesian Networks

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.

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

A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization"

Scalable open-source software to run, develop, and benchmark causal discovery algorithms

DiBS: Differentiable Bayesian Structure Learning, NeurIPS 2021

Python 3.7 version of David Barber's MATLAB BRMLtoolbox

The junction tree algorithm for (discrete) factor graphs

Bayesian Soft Actor Critic

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

dagrad is a Python package that provides an extensible, modular platform for developing and experimenting with differentiable (gradient-based) structure learning methods.

Learning Bayesian Network parameters using Expectation-Maximisation

Distributed Training of Bayesian Neural Networks at Scale

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 …

A solution for deepfake detection across multiple modalities, including images, audio, and video, using ML models like CNNs, Transformers, SVMs, Bayesian networks, and Vision Transformers. This repository includes data preprocessing, model training, evaluation scripts, and Docker support for deployment.

On Feynman--Kac training of partial Bayesian neural networks

Microarray analysis with Bayesian hierarchical clustering and Bayesian network clustering on three microarray datasets. Pearson's correlation coefficient and an augmented Markov blanket are used for feature selection.

Unofficial Implementation of Bayesian Flow Network in easy PyTorch for didactic purposes