Bayes linear calibrated prediction for complex systems

• 成果类型: Article
• 署名作者: Goldstein, Michael; Rougier, Jonathan
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214506000000203
• 发表日期: 2006
• 页码: 1132-1143
• 关键词: models

摘要

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.