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

09/05/2022
by   Laura Schultz, et al.
0

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset