Alexandra Suvorikova, WIAS Berlin
Gaussian process forecast with multidimensional distributional input
In this work, we focus on forecasting a Gaussian process indexed by probability distributions. We introduce a family of positive definite kernels constructed with the use of optimal transportation distance and provide their probabilistic understanding. The technique allows to forecast efficiently Gaussian processes, which opens new perspective in Gaussian process modelling.