High Performance Structured Prediction in PyTorch

Repository for Machine Learning with Probabilistic Programming by Alp Kucukelbir

An implementation of loopy belief propagation for binary image denoising. Both sequential and parallel updates are implemented.

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

This is a collection of algorithms and models written in Python for probabilistic programming. The main focus of the package is on Bayesian reasoning by using Bayesian networks, Markov networks, and their mixing.

opengm quickstart

GUI to help automate functions of the LibRec Java recommendation systems library

In this repository I have calculated average clustering coefficient of a graph generated from the given SNAP dataset using networkX library and compare it with a Erdős–Rényi random graph with the same number of nodes and edge probability as the previous.To plot and compare them i am using matplotlib

Assignments for EECS 491, Spring 2018, CWRU taught by Dr. Michael Lewicki

Cheat sheets

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

Pytorch implementation of Variational Autoencoders - a popular deep generative probabilistic graphical model.

MVA - Probabilistic Graphical Models - Assignments

Identifiability of AMP chain graph

Constructing a Bayesian network to capture the dependencies and independencies among variables as well as to predict wine quality

The project builds a Bayesian Network using pressure sensor data to detect pipe leaks, employing probabilistic reasoning to determine the likelihood of leaks based on sensor readings. It involves loading dataset, defining network structure, calculating CPDs, adding them to model, and using Variable Elimination algorithm for inference

Oncogeriatric synthetic data generator for research purposes.

Some notes on Probabilistic models and advanced ML methods. Implementation of RBM, Contractive AE, Denoising AE and some TS analysis

Deep Generative Models

Create and american sign language recognizer with hidden markov models