em-algorithm

Fast, accurate and scalable probabilistic data linkage using your choice of SQL backend

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

A pytorch package for non-negative matrix factorization.

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

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

machine learning

Implementation of Unsupervised Naive Bayes with EM Algorithm

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.

Markov-Switching State-Space Models

StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

Applied Machine Learning

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

Basic building blocks in Bayesian statistics.

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

Functional Latent datA Models for clusterING heterogeneOus curveS

Julia package for KF and EKF parameter estimation using Automatic Differentiation

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

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

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

This repository contains implementation of Neural Network,k-Means and Gaussian Mixture Models with Python

an EM (Expectation-Maximization) Algorithm sample coin toss 机器学习EM算法例子 抛硬币实验的程序实现

Various machine learning projects using public datasets

Combine partial covariance or relationship matrices

Probabilistic graphical models home works (MVA - ENS Cachan)