Varying Coefficient Models and Design Choice for Bayes Linear Emulation of Complex Computer Models with Limited Model Evaluations

Wilson, Amy L.; Goldstein, Michael; Dent, Chris J.

Abstract

Computer models are widely used to help make decisions about real-world systems. As computer models of large and complex systems can have long run-times and high-dimensional input spaces, it is often necessary to use emulation to assess uncertainties in computer model output. This paper presents methodology for emulation of complex computer models motivated by a real-world example in energy policy. The computer model studied is an economic model of investment in electricity generation in Great Britain. The computer model was used to select parameters in a government policy designed to incentivize investment in renewable technologies to meet government targets. Limited computing time meant that few runs of the computer model were available to fit an emulator. The statistical methodology developed was therefore focused on accurately capturing the uncertainty in computer model output arising from the small number of available model runs. A varying coefficient emulator is proposed to model uncertainty in model output when extrapolating away from model runs. To maximize use of the small number of runs available, this varying coefficient emulator is paired with a criterion-based procedure for design selection.

Citation

Wilson, A. L., Goldstein, M., & Dent, C. J. (2022). Varying Coefficient Models and Design Choice for Bayes Linear Emulation of Complex Computer Models with Limited Model Evaluations. SIAM/ASA Journal on Uncertainty Quantification, 10(1), 350-378. https://doi.org/10.1137/20m1318560

• Journal Article Type: Article
• Acceptance Date: Dec 6, 2021
• Online Publication Date: Mar 7, 2022
• Publication Date: 2022
• Deposit Date: Mar 29, 2022
• Publicly Available Date: Mar 29, 2022
• Journal: SIAM/ASA Journal on Uncertainty Quantification
• Publisher: Society for Industrial and Applied Mathematics
• Peer Reviewed: Peer Reviewed
• Volume: 10
• Issue: 1
• Pages: 350-378
• DOI: https://doi.org/10.1137/20m1318560