Deep Gaussian Mixture Models
Description
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.