A framework for validation of computer models

被引:421
作者
Bayarri, Maria J. [1 ]
Berger, James O.
Paulo, Rui
Sacks, Jerry
Cafeo, John A.
Cavendish, James
Lin, Chin-Hsu
Tu, Jian
机构
[1] Univ Valencia, Dept Stat & OR, E-46100 Burjassot, Valencia, Spain
[2] Univ Tecn Lisboa, ISEG, Dept Matemat, P-1200781 Lisbon, Portugal
[3] Duke Univ, Inst Stat & Decis Sci, Durham, NC 27708 USA
[4] Natl Inst Stat Sci, Res Triangle Pk, NC 27709 USA
[5] Gen Motors, Res & Dev, Warren, MI 48090 USA
基金
美国国家科学基金会;
关键词
Bayesian analysis; identifiability; model discrepancy; prediction;
D O I
10.1198/004017007000000092
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
We present a framework that enables computer model evaluation oriented toward answering the question: Does the computer model adequately represent reality? The proposed validation framework is a six-step procedure based on Bayesian and likelihood methodology. The Bayesian methodology is particularly well suited to treating the major issues associated with the validation process: quantifying multiple sources of error and uncertainty in computer models, combining multiple sources of information, and updating validation assessments as new information is acquired. Moreover, it allows inferential statements to be made about predictive error associated with model predictions in untested situations. The framework is implemented in a test bed example of resistance spot welding, to provide context for each of the six steps in the proposed validation process.
引用
收藏
页码:138 / 154
页数:17
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