University of Cincinnati
154 Hurley Hall
Computer Model Emulation and Calibration using High-dimensional and Non-Gaussian Spatial Data
I will introduce statistical methods to calibrate complex computer models using high-dimensional spatial data sets. This work is motivated by important research problems in climate science where such computer models are frequently used. Computer models play a central role in generating projections of future climate. An important source of uncertainty about future projections from these models is due to uncertainty about input parameters that are key drives of the resulting hindcasts and projections. Computer model calibration is a statistical framework for inferring the input parameters by combining information from computer model runs and observational data. When the data are in the form of high-dimensional spatial fields, computer model emulation (approximation) and calibration can pose significant inferential and computational challenges. The goal of this research is to develop new approaches to computer model calibration that are computationally efficient, accurate, and carefully account for uncertainties.
Originally published at acms.nd.edu.