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Published in:
Bayesian Hierarchical Models for Inference About Population Growth Read chapter Read first chapter

2009 | OriginalPaper | Chapter

Bayes Factors and Multimodel Inference

Authors : William A. Link, Richard J. Barker

Published in: Modeling Demographic Processes In Marked Populations

Publisher: Springer US

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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.

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Metadata
Title
Bayes Factors and Multimodel Inference
Authors
William A. Link
Richard J. Barker
Copyright Year
2009
Publisher
Springer US
Book
Modeling Demographic Processes In Marked Populations

Print ISBN: 978-0-387-78150-1

Electronic ISBN: 978-0-387-78151-8

Copyright Year: 2009

DOI
https://doi.org/10.1007/978-0-387-78151-8_26

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