AMS2005-11: Bayesian Validation of Computer Models for Smooth Functional Data in Hierarchical Contexts

M.J. Bayarri, J.O. Berger, M.C. Kennedy, A. Kottas, R. Paulo, J. Sacks, J.A. Cafeo, C.H. Lin, J. Tu
12/31/2005 09:00 AM
Applied Mathematics & Statistics
A key question in evaluation of computer models is "Does the computer model adequately represent reality?" A complete Bayesian approach to answering this question is developed for the challenging practical context in which the computer model (and reality) produce smooth functional data. The methodology is 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 being able to adapt to different -- but related -- scenarios through hierarchical modeling.

It is also shown how one can formally test if the computer model reproduces reality.

The approach is illustrated through study of a computer model developed to model vehicle crashworthiness.

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