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

Zhou & Stephens (2014) GEMMA multivariate linear mixed model

StAtistical Models for the UnsupeRvised segmentAion of tIme-Series

Basic building blocks in Bayesian statistics.

Functional Latent datA Models for clusterING heterogeneOus curveS

Designing and applying unsupervised learning on the Radar signals to perform clustering using K-means and Expectation maximization for Gausian mixture models to study ionosphere structure. Both the algorithms have been implemented without the use of any built-in packages. The Dataset can be found here: https://archive.ics.uci.edu/ml/datasets/ion…

Mixtures-of-ExperTs modEling for cOmplex and non-noRmal dIsTributionS

single-cell RNA sequencing imputation

Codes for simulation studies to examine the performance of the EM algorithm and its modifications Classification EM and Stochastic EM for Gaussian mixture and a mixture of Markov chains.

🗡️2023/24 Früh­jahrs­se­mes­ter Statistical Models in Computational Biology @ ETHz

An R package for fitting Coxian Phase-Type distributions using the Expectation-Maximization (EM) algorithm. Supports parameter estimation, model selection, survival function computation, and visualization for applications in survival analysis, queueing models, and reliability engineering.

EM algorithm for improving factors found with principle component method

project to deploy EM Algorithm as a shiny app

Statistical project on the Expectation-Maximization algorithm applied to gaussian pooling - ENSAE ParisTech

R package for clustering continuous and categorical data, using mixture models.

Ph.D. Dissertation

Simple-Supervised Factor Analysis

R implementations of a variational EM approach for Stochastic Block Model with covariates