Generative deep Gaussian processes

In this paper, the possibilities of using combinations of Gaussian models for the problems of describing two-dimensional models are investigated. It is proposed to use multidimensional deep Gaussian models as the basis for such a description. The tasks are formalized, the solution of which is necessary for the correct training of these models from single images. In the framework of solving these problems, a consistent Bayesian derivation of the parameters of the corresponding deep Gaussian models was performed. In the framework of experiments to simulate images of different types, the consistency of the found relations is shown