Jim Berger
Institute of Statistics and Decision Sciences
Duke University
Statistical Validation of Computer Models
One of the major activities in science and engineering today is the development of math-based computer models of
scientific and engineering processes. The most basic question in the evaluation of such computer models is "Does the computer
model adequately represent reality?"
A six-step computer model validation methodology with be discussed. The methodology is based on using spatial and Bayesian
statistical tools. The latter are particularly 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, hierarchical Bayesian techniques allow inferential statements to
be made about predictive error associated with model predictions in untested situations.
The framework has been implemented for two test bed computer models (a vehicle crash model and a resistance spot
welding model) that will be used to illustrate the proposed validation process.
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