AN ADDITIVE GLOBAL AND LOCAL GAUSSIAN PROCESS MODEL FOR LARGE DATA SETS

Many computer models of large complex systems are time consuming to experiment on. Even when surrogate models are developed to approximate the computer models, estimating an appropriate surrogate model can still be computationally challenging. In this article, we propose an Additive Global and Local Gaussian Process (AGLGP) model as a flexible surrogate for stochastic computer models. This model attempts to capture the overall global spatial trend and the local trends of the responses separately. The proposed additive structure reduces the computational complexity in model fitting, and allows for more efficient predictions with large data sets. We show that this metamodel form is effective in modelling various complicated stochastic model forms.