A simple but generic implementation of Expectation Maximization algorithms to fit mixture models.

An R package for predicting ploidal level from sequence data using site-based heterozygosity 

Halfmoons dataset - perceptron, least squares, GMM and RBF solutions.

A LibreOffice Calc extension that clusters the rows in a table and colors them to indicate the clusters.

R project for comparing different Missing Value Imputation (MVI)* approaches across three datasets.

A Python package following the scikit-learn API for model-based clustering and generalized mixture modeling (latent class/profile analysis) of continuous and categorical data. StepMix handles missing values through Full Information Maximum Likelihood (FIML) and provides multiple stepwise Expectation-Maximization (EM) estimation methods.

EM algorithm for probabilistic PCA in Python

The Expectation Maximization (EM) algorithm is used to reduce Poisson noise in CT images. The repository provides derivations and evaluations with the Cramer-Rao Lower Bound (CRLB).

Superiority of Quadratic Over Conventional Neural Networks for Classification of Gaussian Mixture Data

Learning Diffusion Priors from Observations by Expectation Maximization

Containing various ML projects, including image and text classification, clustering, regression, and neural networks. Projects include implementations of Random Forest, SVM, Decision Trees, EM algorithms, and advanced models like MLP and CNN, with datasets ranging from facial emotion detection to ECG signals and more.

Implementing the Expectation-Maximization algorithm and applies Gaussian Mixture Models (GMM) to classify images.

This project demonstrates the segmentation of images using a Gaussian Mixture Model (GMM) and the Expectation-Maximization (EM) algorithm. The project applies these advanced machine learning techniques to segment both grayscale and color images, providing a comprehensive approach to image segmentation.

Machine Learning course, Spring 2024, University of Tehran, Dr. M Tavassolipour and Dr. MRA Dehaqani

Machine Learning 2018 course work (Testate)

The multi-sample Gaussian mixture model (MSGMM) is a clustering model adapted to fitting multiple samples simultaneously using the EM algorithm.

collection of numerical optimization methods

Expectation Maximisation for a Gaussian Mixture Model Implemetation of the expectation maximisation algorithm for Gaussian Mixture Models in C++

This repository is for the ICML'24 paper: "Unveiling the Cycloid Trajectory of EM Iterations in Mixed Linear Regression"

R package for fitting mixture distributions to data using various approaches