Bayes Linear Calibrated Prediction for Complex Systems

Goldstein, M.; Rougier, J.C.

Abstract

A calibration-based approach is developed for predicting the behavior of a physical system that is modeled by a computer simulator. The approach is based on Bayes linear adjustment using both system observations and evaluations of the simulator at parameterizations that appear to give good matches to those observations. This approach can be applied to complex high-dimensional systems with expensive simulators, where a fully Bayesian approach would be impractical. It is illustrated with an example concerning the collapse of the thermohaline circulation (THC) in the Atlantic Ocean.

Citation

Goldstein, M., & Rougier, J. (2006). Bayes Linear Calibrated Prediction for Complex Systems. Journal of the American Statistical Association, 101(475), 1132-1143. https://doi.org/10.1198/016214506000000203

• Journal Article Type: Article
• Publication Date: Sep 1, 2006
• Deposit Date: Apr 25, 2007
• Publicly Available Date: Aug 12, 2016
• Journal: Journal of the American Statistical Association
• Print ISSN: 0162-1459
• Electronic ISSN: 1537-274X
• Publisher: Taylor and Francis Group
• Peer Reviewed: Peer Reviewed
• Volume: 101
• Issue: 475
• Pages: 1132-1143
• DOI: https://doi.org/10.1198/016214506000000203
• Keywords: Emulator, Calibration, Hat run, Thermohaline circulation (THC).
• Public URL: https://durham-repository.worktribe.com/output/1564775
• Related Public URLs: http://www.maths.dur.ac.uk/stats/people/jcr/BLCP-a4.pdf