Brazilian Journal of Probability and Statistics

Predictive construction of priors in Bayesian nonparametrics

Sandra Fortini and Sonia Petrone

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

First available in Project Euclid: 3 July 2012

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


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.

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