Modularization in Bayesian analysis, with emphasis on analysis of computer models

Abstract

Bayesian analysis incorporates different sources of information into a single analysis through Bayes theorem. When one or more of the sources of information are suspect (e.g., if the model assumed for the information is viewed as quite possibly being significantly flawed), there can be a concern that Bayes theorem allows this suspect information to overly influence the other sources of information. We consider a variety of situations in which this arises, and give methodological suggestions for dealing with the problem.

After consideration of some pedagogical examples of the phenomenon, we focus on the interface of statistics and the development of complex computer models of processes. Three testbed computer models are considered, in which this type of issue arises.