Showing papers by "James O. Berger published in 2006"
TL;DR: It is suggested that the statistical community should accept formal objective Bayesian techniques with confidence, but should be more cautious about casual objectiveBayesian techniques.
Abstract: Bayesian statistical practice makes extensive use of versions of ob- jective Bayesian analysis. We discuss why this is so, and address some of the criticisms that have been raised concerning objective Bayesian analysis. The dan- gers of treating the issue too casually are also considered. In particular, we suggest that the statistical community should accept formal objective Bayesian techniques with confldence, but should be more cautious about casual objective Bayesian techniques.
659 citations
TL;DR: The issue of prior specification for such multiple tests; computation of key posterior quantities; and useful ways to display these quantities are studied.
364 citations
01 Jan 2006
TL;DR: Objective Bayesian inference for the multivariate normal distribution is illustrated, using different types of formal objective priors (Jeffreys, invariant, reference and matching), different modes of inference (Bayesian and frequentist), and different criteria involved in selecting optimal objective priours.
Abstract: Objective Bayesian inference for the multivariate normal distribution is illustrated, using different types of formal objective priors (Jeffreys, invariant, reference and matching), different modes of inference (Bayesian and frequentist), and different criteria involved in selecting optimal objective priors (ease of computation, frequentist performance, marginalization paradoxes, and decision-theoretic evaluation).
41 citations