deepgmm: Deep Gaussian Mixture Models

Deep Gaussian mixture models as proposed by Viroli and McLachlan (2019) <doi:10.1007/s11222-017-9793-z> provide a generalization of classical Gaussian mixtures to multiple layers. Each layer contains a set of latent variables that follow a mixture of Gaussian distributions. To avoid overparameterized solutions, dimension reduction is applied at each layer by way of factor models.

Getting started

Package details

AuthorCinzia Viroli, Geoffrey J. McLachlan
MaintainerSuren Rathnayake <>
LicenseGPL (>= 3)
Package repositoryView on CRAN
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deepgmm documentation built on Nov. 21, 2022, 1:05 a.m.