Nonsubjective Bayes testing - an overview

In Bayesian model selection, or hypothesis testing, difficulties arise when improper noninformative priors are used to calculate the Bayes factors. Several methods have been proposed to remove these difficulties. In this paper we discuss a unified derivation of some of these methods which shows that in some qualitative or conceptual sense, these methods are no more than a fixed number of observations away from a Bayes factor based on noninformative priors, and are close to each other and to certain Bayes factors based on low information proper priors which include priors recommended by Jeffreys (1961). (C) 2002 Elsevier Science B.V. All rights reserved.