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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.
| 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 |
| Google Analytic Metrics | Metrics page |