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.
|Author||Cinzia Viroli, Geoffrey J. McLachlan|
|Maintainer||Suren Rathnayake <[email protected]>|
|License||GPL (>= 3)|
|Package repository||View on CRAN|
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