Modularization in Bayesian analysis, with emphasis on analysis of computer models
Authors:
- M. J. BayarriMBM. J. Bayarri
- J. O. BergerJBJ. O. Berger
- F. LiuFLF. Liu
Top Cited Papers
Open Access
- 1 March 2009
- journal article
- Published by Institute of Mathematical Statistics in Bayesian Analysis
- Vol. 4 (1) , 119-150
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.Keywords
This publication has 22 references indexed in Scilit:
- Bayesian Treed Gaussian Process Models With an Application to Computer ModelingJournal of the American Statistical Association, 2008
- Bayesian Hierarchical Modeling for Integrating Low-Accuracy and High-Accuracy ExperimentsTechnometrics, 2008
- A Framework for Validation of Computer ModelsTechnometrics, 2007
- Checking for prior-data conflictBayesian Analysis, 2006
- On Model Expansion, Model Contraction, Identifiability and Prior Information: Two Illustrative Scenarios Involving Mismeasured VariablesStatistical Science, 2005
- Bayesian Forecasting for Complex Systems Using Computer SimulatorsJournal of the American Statistical Association, 2001
- Bayesian Design and Analysis of Computer Experiments: Use of Derivatives in Surface PredictionTechnometrics, 1993
- Bayesian Prediction of Deterministic Functions, with Applications to the Design and Analysis of Computer ExperimentsJournal of the American Statistical Association, 1991
- Sampling-Based Approaches to Calculating Marginal DensitiesJournal of the American Statistical Association, 1990
- Design and Analysis of Computer ExperimentsStatistical Science, 1989