Deep Gaussian processes: Theory and applications

Abstract

Gaussian processes are an infinite-dimensional generalization of multivariate normal distributions. They provide a principled approach to learning with kernel machines and they have found wide applications in many fields. More recently, with the advance of deep learning, the concept of deep Gaussian processes has emerged. Deep Gaussian processes have improved capacity for prediction and classification over standard Gaussian processes and models based on them preserve the features of allowing for full Bayesian treatment and for applications when the amount of available data is limited. The theory of recent progress in deep Gaussian processes will be presented and selected applications to problems in medicine will be provided.