Deep Gaussian Mixture Models

Deep Gaussian mixture models as proposed by Viroli and McLachlan (2019) 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.


Reference manual

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0.1.62 by Suren Rathnayake, a year ago

Browse source code at

Authors: Cinzia Viroli , Geoffrey J. McLachlan

Documentation:   PDF Manual  

GPL (>= 3) license

Imports mvtnorm, corpcor, mclust

Suggests testthat

See at CRAN