Fast, accurate and scalable probabilistic data linkage with support for multiple SQL backends

Decision Trees, Random Forest, Dynamic Time Warping, Naive Bayes, KNN, Linear Regression, Logistic Regression, Mixture Of Gaussian, Neural Network, PCA, SVD, Gaussian Naive Bayes, Fitting Data to Gaussian, K-Means

Collection of EM algorithms for blind source separation of audio signals

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

A pytorch package for non-negative matrix factorization.

R code for Time Series Analysis and Its Applications, Ed 4

R package to accompany Time Series Analysis and Its Applications: With R Examples -and- Time Series: A Data Analysis Approach Using R

Multivariate and Multichannel Discrete Hidden Markov Models for Categorical Sequences

Fast and space-efficient taxonomic classification of long reads

[MICCAI 2022 Best Paper Finalist] Bayesian Pseudo Labels: Expectation Maximization for Robust and Efficient Semi Supervised Segmentation

machine learning

Python+Rust implementation of the Probabilistic Principal Component Analysis model

No description provided.

This repository is for sharing the scripts of EM algorithm and variational bayes.

Markov-Switching State-Space Models

Implementation of Unsupervised Naive Bayes with EM Algorithm

Zhou & Stephens (2014) GEMMA multivariate linear mixed model

gmm_diag and gmm_full: C++ classes for multi-threaded Gaussian mixture models and Expectation-Maximisation

StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

A Unified RNA Sequencing Model (URSM) for joint analysis of single cell and bulk RNA-seq data.

Wine Types Clustering using K-Means, EM-GMM and PCA

An(other) implementation of Explicit Duration Hidden Semi-Markov Models in Python 3

Fully supervised binary classification of skin lesions from dermatoscopic images using multi-color space moments/texture features and Support Vector Machines/Random Forests.

No description provided.

PySATL module providing tools for working with mixtures of distributions. In particular, it allows you to evaluate their parameters.

An interactive toolkit for visualizing GMM convergence in 3D/2D, featuring PCA for dimensionality reduction, K-means++ initialization, and covariance regularization for stability.

EM algorithm to estimate the traffic volume using connected vehicle trajectory, which was proposed by Zheng and Liu.

Applied Machine Learning

Probabilistic graphical models home works (MVA - ENS Cachan)

Implementation of EM using K-Means(Gaussian Mixture Model)