2018/01/26 by Marko Nagode, Nagode, Marko
Computer Science · Mathematics · #62F10 #62H30 #Algorithms and Data Compression #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #cs.LG #msc:62F10 #msc:62H30 #stat.ML
paper · pdf · doi:10.48550/arxiv.1801.08788
15 pages, 6 figures, R code
arxiv created 2018/01/26 · openalex publication_date 2018/01/26 · arxiv updated 2018/01/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The rebmix package provides R functions for random univariate and multivariate finite mixture model generation, estimation, clustering and classification. The paper is focused on multivariate normal mixture models with unrestricted variance-covariance matrices. The objective is to show how to generate datasets for a known number of components, numbers of observations and component parameters, how to estimate the number of components, component weights and component parameters and how to predict cluster and class membership based upon a model trained by the REBMIX algorithm. The accompanying plotting, bootstrapping and other features of the package are dealt with, too. For demonstration purpose a multivariate normal dataset with unrestricted variance-covariance matrices is studied.