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
Edited by: David L. Thomson, Evan G. Cooch, and 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.
Suggested Citation
Link, W., Barker, R.J., 2009, Bayes factors and multimodel inference, chap. of Modeling demographic processes in marked populations, p. 595-615.
| 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 |