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Bayesian Theory and Applications$
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Paul Damien, Petros Dellaportas, Nicholas G. Polson, and David A. Stephens

Print publication date: 2013

Print ISBN-13: 9780199695607

Published to Oxford Scholarship Online: May 2013

DOI: 10.1093/acprof:oso/9780199695607.001.0001

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Bayesian model averaging in the M-open framework

Bayesian model averaging in the M-open framework

Chapter:
(p.483) 24 Bayesian model averaging in the M-open framework
Source:
Bayesian Theory and Applications
Author(s):

Merlise Clydec

Edwin S Iversen

Publisher:
Oxford University Press
DOI:10.1093/acprof:oso/9780199695607.003.0024

This chapter presents a model averaging approach in the M-open setting using sample re-use methods to approximate the predictive distribution of future observations. It first reviews the standard M-closed Bayesian Model Averaging approach and decision-theoretic methods for producing inferences and decisions. It then reviews model selection from the M-complete and M-open perspectives, before formulating a Bayesian solution to model averaging in the M-open perspective. It constructs optimal weights for MOMA:M-open Model Averaging using a decision-theoretic framework, where models are treated as part of the ‘action space’ rather than unknown states of nature. Using ‘incompatible’ retrospective and prospective models for data from a case-control study, the chapter demonstrates that MOMA gives better predictive accuracy than the proxy models. It concludes with open questions and future directions.

Keywords:   multiple proxy models, M-open framework, Bayesian inference, model averaging

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