A framework for validation of computer models

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. © 2007 American Statistical Association and the American Society for Quality.

Duke Scholars

Author James O. Berger Statistical Science