Title:

EzGP: Easy-to-Interpret Gaussian Process Models for Computer Experiments with Both Quantitative and Qualitative Factors.

Abstract:

Computer experiments with both quantitative and qualitative (QQ) inputs are commonly used in science and engineering applications. Constructing desirable emulators for such computer experiments remains a challenging problem. In this article, we propose an easy-to-interpret Gaussian process (EzGP) model for computer experiments to reflect the change of the computer model under different level combinations of qualitative factors. The proposed modeling strategy, based on an additive Gaussian process (GP), is flexible to address the heterogeneity of computer models involving multiple  qualitative factors. We also develop two useful variants of the EzGP model to achieve computation efficiency when dealing with high dimensional data and large data size. The merits of these models are illustrated by a real data application and several numerical examples.

Bio:

Dr. Qian Xiao is currently an assistant professor in statistics at University of Georgia. He received his Ph.D. at UCLA in 2017. His research interests are experimental design, stochastic modeling and pharmaceutical studies.