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June 2013 Bayesian Nonparametric Inference – Why and How
Peter Müller, Riten Mitra
Bayesian Anal. 8(2): 269-302 (June 2013). DOI: 10.1214/13-BA811

Abstract

We review inference under models with nonparametric Bayesian (BNP) priors. The discussion follows a set of examples for some common inference problems. The examples are chosen to highlight problems that are challenging for standard parametric inference. We discuss inference for density estimation, clustering, regression and for mixed effects models with random effects distributions. While we focus on arguing for the need for the flexibility of BNP models, we also review some of the more commonly used BNP models, thus hopefully answering a bit of both questions, why and how to use BNP.

This review was sponsored by the Bayesian Nonparametrics Section of ISBA (ISBA/BNP). The authors thank the section officers for the support and encouragement.

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Peter Müller. Riten Mitra. "Bayesian Nonparametric Inference – Why and How." Bayesian Anal. 8 (2) 269 - 302, June 2013. https://doi.org/10.1214/13-BA811

Information

Published: June 2013
First available in Project Euclid: 24 May 2013

zbMATH: 1329.62171
MathSciNet: MR3066939
Digital Object Identifier: 10.1214/13-BA811

Keywords: dependent Dirichlet process , Dirichlet process , nonparametric models , Polya tree

Rights: Copyright © 2013 International Society for Bayesian Analysis

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Vol.8 • No. 2 • June 2013
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