  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 the
different level combinations of qualitative factors. The proposed modeling
strategy, based on an additive Gaussian process, 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 computational
efficiency for data with high dimensionality and large sizes. The merits of
these models are illustrated by several numerical examples and a real data
application.
