Bayesian model selection with improper priors is not well-defined becauseof the dependence of the marginal likelihood on the arbitrary scaling constantsof the within-model prior densities. We show how this problem can beevaded by replacing marginal log-likelihood by a homogeneous proper scoring rule,which is insensitive to the scaling constants. Suitably applied, this will typicallyenable consistent selection of the true model.
Titolo: | Bayesian model selection based on proper scoring rules |
Autori: | |
Data di pubblicazione: | 2015 |
Rivista: | |
Handle: | http://hdl.handle.net/11584/186178 |
Tipologia: | 1.1 Articolo in rivista |
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