Brazilian Journal of Probability and Statistics

Predictive construction of priors in Bayesian nonparametrics

Sandra Fortini and Sonia Petrone

Full-text: Open access

Abstract

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.

Article information

Source
Braz. J. Probab. Stat., Volume 26, Number 4 (2012), 423-449.

Dates
First available in Project Euclid: 3 July 2012

Permanent link to this document
https://projecteuclid.org/euclid.bjps/1341320251

Digital Object Identifier
doi:10.1214/11-BJPS176

Mathematical Reviews number (MathSciNet)
MR2949087

Zentralblatt MATH identifier
1319.62075

Keywords
Exchangeability Dirichlet process random probability measures mixtures of Markov chains infinite hidden Markov models urn schemes

Citation

Fortini, Sandra; Petrone, Sonia. Predictive construction of priors in Bayesian nonparametrics. Braz. J. Probab. Stat. 26 (2012), no. 4, 423--449. doi:10.1214/11-BJPS176. https://projecteuclid.org/euclid.bjps/1341320251


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