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|
|Data di pubblicazione:||2015|
|Tipologia:||1.1 Articolo in rivista|