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

2022/09/05 by Laura Luise Schultz, Schultz, Laura, Vadim Sokolov +1 · 1 citation
Computer Science · #Applications (stat.AP) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference

paper · pdf · doi:10.48550/arxiv.2209.02163

openalex publication_date 2022/09/05 · openalex created_date 2022/09/08 · openalex updated_date 2026/07/28

Abstract

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

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