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Bayes factors and multimodel inference

Proceedings of the 2007 EURING Technical Meeting and Workshop held January 14-20, 2007 in Dunedin, New Zealand. OCLC: 213382236 PDF on file: 7054_Link.pdf
By:
,
Edited by:
David L. Thomson, Evan G. Cooch, Michael J. Conroy

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Abstract

Multimodel inference has two main themes: model selection, and model averaging. Model averaging is a means of making inference conditional on a model set, rather than on a selected model, allowing formal recognition of the uncertainty associated with model choice. The Bayesian paradigm provides a natural framework for model averaging, and provides a context for evaluation of the commonly used AIC weights. We review Bayesian multimodel inference, noting the importance of Bayes factors. Noting the sensitivity of Bayes factors to the choice of priors on parameters, we define and propose nonpreferential priors as offering a reasonable standard for objective multimodel inference.

Additional Publication Details

Publication type:
Book chapter
Publication Subtype:
Book Chapter
Title:
Bayes factors and multimodel inference
Series number:
3
Year Published:
2009
Language:
English
Publisher:
Springer
Publisher location:
New York and London
Contributing office(s):
Patuxent Wildlife Research Center
Description:
xxiv, 1136
Larger Work Type:
Book
Larger Work Subtype:
Other Government Series
Larger Work Title:
Modeling demographic processes in marked populations
First page:
595
Last page:
615