Open Access
June 2018 Prior Distributions for Objective Bayesian Analysis
Guido Consonni, Dimitris Fouskakis, Brunero Liseo, Ioannis Ntzoufras
Bayesian Anal. 13(2): 627-679 (June 2018). DOI: 10.1214/18-BA1103


We provide a review of prior distributions for objective Bayesian analysis. We start by examining some foundational issues and then organize our exposition into priors for: i) estimation or prediction; ii) model selection; iii) high-dimensional models. With regard to i), we present some basic notions, and then move to more recent contributions on discrete parameter space, hierarchical models, nonparametric models, and penalizing complexity priors. Point ii) is the focus of this paper: it discusses principles for objective Bayesian model comparison, and singles out some major concepts for building priors, which are subsequently illustrated in some detail for the classic problem of variable selection in normal linear models. We also present some recent contributions in the area of objective priors on model space. With regard to point iii) we only provide a short summary of some default priors for high-dimensional models, a rapidly growing area of research.


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Guido Consonni. Dimitris Fouskakis. Brunero Liseo. Ioannis Ntzoufras. "Prior Distributions for Objective Bayesian Analysis." Bayesian Anal. 13 (2) 627 - 679, June 2018.


Published: June 2018
First available in Project Euclid: 14 April 2018

zbMATH: 06989961
MathSciNet: MR3807861
Digital Object Identifier: 10.1214/18-BA1103

Primary: 62-02 , 62F15
Secondary: 62A01 , 62J05

Keywords: criteria for model choice , high-dimensional model , model comparison , noninformative prior , objective Bayes , reference prior , Variable selection

Vol.13 • No. 2 • June 2018
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