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March 2009 Modularization in Bayesian analysis, with emphasis on analysis of computer models
M. J. Bayarri, J. O. Berger, F. Liu
Bayesian Anal. 4(1): 119-150 (March 2009). DOI: 10.1214/09-BA404

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.

Citation

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M. J. Bayarri. J. O. Berger. F. Liu. "Modularization in Bayesian analysis, with emphasis on analysis of computer models." Bayesian Anal. 4 (1) 119 - 150, March 2009. https://doi.org/10.1214/09-BA404

Information

Published: March 2009
First available in Project Euclid: 22 June 2012

zbMATH: 1330.65033
MathSciNet: MR2486241
Digital Object Identifier: 10.1214/09-BA404

Rights: Copyright © 2009 International Society for Bayesian Analysis

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Vol.4 • No. 1 • March 2009
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