BAYES FACTORS AND MARGINAL DISTRIBUTIONS IN INVARIANT SITUATIONS

By Luis R. Pericchi, Julia A. Varshavsky. 2016

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A Tutorial on Fisher Information (2017, background): “…of radius two is given by 184π2 2 = 2π.2 Orthogonality is relevant in Bayesian analysis, as it provides an argument to choose priors on a3 vector-valued parameter independently (e.g., Berger et al., 1998; Huzurbazar, 1950, 1956; Jeffreys,4 1961; Kass and Vaidyanathan, 1992; Ly et al.,…”
The expected demise of the Bayes factor (2015, background): “…uncomfortable with, since those improper priors are not justified (DeGroot, 1982) in most testing situations, leading to many alternative if ad hoc solutions (Robert, 2001), where data is either used twice (Aitkin, 2010) or split in artificial ways (Berger and Pericchi, 1996; Berger et al., 1998).”
Testing hypotheses via a mixture estimation model (2014, methodology): “…discussed in more detail below, has led to the proliferation of alternative ad hoc solutions, where the data are either used twice (Aitkin, 1991, 2010; Gelman et al., 2013b) or split in artificial ways (O’Hagan, 1995; Berger and Pericchi, 1996; Berger et al., 1998; Berger and Pericchi, 2001).”