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Bayes Linear Calibrated Prediction for Complex Systems

Goldstein, M.; Rougier, J.C.

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Authors

J.C. Rougier



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).
Related Public URLs http://www.maths.dur.ac.uk/stats/people/jcr/BLCP-a4.pdf

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