Comparing Computer Experiments for the Gaussian Process Model Using Integrated Prediction Variance

Abstract

ABSTRACT Space-filling designs are a common choice of experimental design strategy for computer experiments. This article compares space-filling design types based on their theoretical prediction variance properties with respect to the Gaussian process model. An analytical solution for calculating the integrated prediction variance (IV) of the Gaussian process model is given. Using the analytical calculation of IV as a response variable, this article presents a study of the effects of dimension; sample size; value of parameter vector, θ; and experimental design type using a factorial design and regression analysis.