opengm quickstart

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

This is an example implementation of a graphical model in the domain of image denoising

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

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

NLP Models

Source code for the paper "Lifted Model Construction without Normalisation: A Vectorised Approach to Exploit Symmetries in Factor Graphs" (LoG 2024)

Personal repository for COL786. Pardon mistakes!

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

Moved to https://github.com/Tuebel/BayesNet.jl

Create and american sign language recognizer with hidden markov models

Source code for the paper "Approximate Lifted Model Construction" (IJCAI 2025)

C# based experiments in probabilistic graphical models

This unit provides a strong background in the analysis of multivariate and categorical data. Concepts such as probability theory, Bayesian modelling, dimensionality reduction, clustering, finite mixture modelling and probabilistic graphical models form the core knowledge of this unit.

Discrete factor operations for probabilistic graphical models

Source code for the paper "Lifting Factor Graphs with Some Unknown Factors for New Individuals" (IJAR 2025)

Built a system that can recognize words communicated using the American Sign Language (ASL). Trained a set of Hidden Markov Models (HMMs) using part of a preprocessed dataset of tracked hand and nose positions extracted from video to try and identify individual words from test sequences. Experimented with model selection techniques including BIC…

Undirected graphical models are compact representations of joint probability distributions over random variables. To solve inference tasks of interest, graphical models of arbitrary topology can be trained using empirical risk minimization. However, to solve inference tasks that were not seen during training, these models (EGMs) often need to be…