Gaussian process regression was designed for problems with strictly numeric predictor variables. However, GPR can be used with categorical predictor variables by using one-hot encoding.

In this article, we propose a generalized Gaussian process concurrent regression model for functional data, where the functional response variable has a binomial. Poisson, or other non-Gaussian ...

Our approach avoids nested simulation or simulation and regression of cashflows by learning a Gaussian metamodel for the mark-to-market cube of a derivative portfolio. We model the joint posterior of ...

The model based on Gaussian process (GP) prior and a kernel covariance function can be used to fit nonlinear data with multidimensional covariates. It has been used as a flexible nonparametric ...

Machine learning hedge strategy with deep Gaussian process regression An optimal hedging strategy for options in discrete time using a reinforcement learning technique ...