SPEAKER:
Susie Bayarri, Valencia/SAMSI/Duke
Title: Statistical Validation
of Computer Simulators
ABSTRACT:
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Computer implementation of math-based models (simulators) is increasingly
used in many areas; an important question that arises is whether the model adequately represents reality. We propose a six-step framework for model validation. Bayesian methods 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. However, Bayesian analyses for complex models are usually implemented via Monte Carlo (MC) or Markov chain Monte Carlo (MCMC) methods, requiring thousands of computer model runs, making it infeasible for slow simulators. We have successfully used response surface approximations to the models for the purpose of running the MCMC. These purely statistical approaches use the model as "black boxes", and are thus most appropriate when the code cannot be accessed and/or manipulated. COFFEE: 3:45 p.m., 104 Snedecor Hall |