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, F…

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

[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

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

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

StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

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

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