A Framework for Validation of Computer Models

Autor:	James O. Berger, Chin-Hsu Lin, Jian Tu, Maria J. Bayarri, Rui Paulo, James C. Cavendish, J Sacks, John A. Cafeo
Rok vydání:	2007
Předmět:	Statistics and Probability Validation rule Computer science Applied Mathematics Modeling and Simulation Bayesian probability Identifiability Data mining Sources of error computer.software_genre computer Gaussian process emulator
Zdroj:	Technometrics. 49:138-154
ISSN:	1537-2723 0040-1706
DOI:	10.1198/004017007000000092
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d18811dddfd2ec305155652aa8085ad8 https://doi.org/10.1198/004017007000000092 Zobrazit plný text záznamu