R package to accompany Time Series Analysis and Its Applications: With R Examples -and- Time Series: A Data Analysis Approach Using R
-
Updated
Sep 28, 2024 - R
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
StAtistical Models for the UnsupeRvised segmentAion of tIme-Series
Zhou & Stephens (2014) GEMMA multivariate linear mixed model
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.
Generalised Latent Variable Models for Location, Shape, and Scale
2023/24 Frühjahrssemester Statistical Models in Computational Biology @ ETHz
EM algorithm for improving factors found with principle component method
project to deploy EM Algorithm as a shiny app
R package for clustering continuous and categorical data, using mixture models.
Ph.D. Dissertation
R implementations of a variational EM approach for Stochastic Block Model with covariates
Statistical project on the Expectation-Maximization algorithm applied to gaussian pooling - ENSAE ParisTech
Simple-Supervised Factor Analysis
Add a description, image, and links to the em-algorithm topic page so that developers can more easily learn about it.
To associate your repository with the em-algorithm topic, visit your repo's landing page and select "manage topics."