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Deep Learning Gaussian Processes (DL-GP) are proposed as a methodology foranalyzing (approximating) computer models that produce heteroskedastic andhigh-dimensional output. Computer simulation models have many areas ofapplications, including social-economic processes, agriculture, environmental,biology, engineering and physics problems. A deterministic transformation ofinputs is performed by deep learning and predictions are calculated bytraditional Gaussian Processes. We illustrate our methodology using asimulation of motorcycle accidents and simulations of an Ebola outbreak.Finally, we conclude with directions for future research.

Deep Learning Gaussian Processes For Computer Models with Heteroskedastic and High-Dimensional Outputs