Bayes factors and multimodel inference
June 9, 2009
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.
Citation Information
Publication Year | 2009 |
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Title | Bayes factors and multimodel inference |
Authors | W. A. Link, R. J. Barker |
Publication Type | Book Chapter |
Publication Subtype | Book Chapter |
Series Number | 3 |
Index ID | 5211454 |
Record Source | USGS Publications Warehouse |
USGS Organization | Patuxent Wildlife Research Center |