Unsupervised Clustering and Meta-analysis using Gaussian Mixture Copula Models

Estimation and inference for Gaussian Mixture Models where the inputs contain missing values.

Gaussian Parsimonious Clustering Models with Gating and Expert Network Covariates

Sparse Bayesian ARX models with flexible noise distributions

The code for fitting a mixture distribution to data and Gaussian Mixture Model (GMM)

Gaussian Mixture Graphical Model Learning and Inference

The multi-sample Gaussian mixture model (MSGMM) is a clustering model adapted to fitting multiple samples simultaneously using the EM algorithm.

Clinical significance of sex hormone levels in testis carcinoma

Supplementary information to the book chapter "Spatially-aware unsupervised classification of time-series data using a hybrid approach".

Bayesian framework for more stable and identifiable mixture models inference with covariates

Cluster analysis of shape features in famous datasets

R package for entropy-informed detection of emerging viral variants and genomic surveillance. Implements per-site Shannon entropy, Gaussian mixture model site selection, Hellinger distance, and non-parametric change-point detection. Validated on SARS-CoV-2 Spike protein sequences from NCBI and GISAID databases.

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

Re-implementation of the SSC-UC method proposed by Schrunner et al. (2020)

A mixture of Gaussians generator written in R. It generates Gaussian distributions with fixed standard deviation in each dimension (spherical clusters) and calculates the C-separability index described in "Learning mixtures of Gaussians" (Dasgupta, 1999)

Bayesian Gaussian mixture models with Gibbs sampling and variational inference in R

Implementing book mixtures according to provided recipes !

Corrects effect sizes estimates for winner's curse.

R package for maximal likelihood estimation of multivariate normal mixture models