Open Access
November 2012 Predictive construction of priors in Bayesian nonparametrics
Sandra Fortini, Sonia Petrone
Braz. J. Probab. Stat. 26(4): 423-449 (November 2012). DOI: 10.1214/11-BJPS176


The characterization of models and priors through a predictive approach is a fundamental problem in Bayesian statistics. In the last decades, it has received renewed interest, as the basis of important developments in Bayesian nonparametrics and in machine learning. In this paper, we review classical and recent work based on the predictive approach in these areas. Our focus is on the predictive construction of priors for Bayesian nonparametric inference, for exchangeable and partially exchangeable sequences. Some results are revisited to shed light on theoretical connections among them.


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Sandra Fortini. Sonia Petrone. "Predictive construction of priors in Bayesian nonparametrics." Braz. J. Probab. Stat. 26 (4) 423 - 449, November 2012.


Published: November 2012
First available in Project Euclid: 3 July 2012

zbMATH: 1319.62075
MathSciNet: MR2949087
Digital Object Identifier: 10.1214/11-BJPS176

Keywords: Dirichlet process , exchangeability , infinite hidden Markov models , mixtures of Markov chains , random probability measures , urn schemes

Rights: Copyright © 2012 Brazilian Statistical Association

Vol.26 • No. 4 • November 2012
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