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\centerline{\bf STANFORD UNIVERSITY}
\centerline{\bf DEPARTMENT OF STATISTICS}
\centerline{\big DEPARTMENTAL SEMINAR}
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\centerline{4:15 p.m., Tuesday, February 11, 2003}
\centerline{Sequoia Hall Room 200}
\centerline{(Cookies at 3:45 in 1st Floor Lounge)}

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\centerline{\sl Jerome Sacks}
\centerline{\sl NISS}
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\centerline{\bf Statistical validation of computer models}
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The use of mathematically based computer models for the study of
scientific
and engineering processes is ubiquitous. The most basic question in
evaluating such a model is: "Does the computer model adequately
represent
reality?"

Statistical methodology for addressing this question will be described
within the context of test-bed problems. The proposed six-step strategy
deals with major issues associated with a validation process:
quantifying
the typically multiple sources of error and uncertainty in computer
models,
combining multiple sources of information (e.g., from field experiments
and
computer runs), calibrating parameters of the computer model, and
assessing
model predictions in untested situations. A combination of spatial and
bayesian statistical tools provides the technical apparatus.

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