Using The Cross-Validation Method to Predict Computer Model Outputs Using The Stochastic Gaussian Process Regression Model

Computer models (CMs), which are called simulators, may be computationally expensive. Gaussian process regression models (GPRMs) are statistical models that have been used as a surrogate for expensive computer models. GPRMs are built based on assumptions similar to any statistical model. Thus, we must verify the validity of GPRMs before using them. In this paper, we use GPRMs for stochastic computer models (CMs). Then, we discussed how to verify the GPRMs for stochastic CMs by using the Cross-Validation method to predict the GPRMs. The stochastic GPRM was applied to a real CM represented by OTL Circuit function as an illustrative example. The accuracy of the GPRM was verified using stochastic measures, and it was verified that the GPRM is suitable as an alternative to the complex CM.