Open Access
February 2004 Nonparametric Bayesian Data Analysis
Peter Müller, Fernando A. Quintana
Statist. Sci. 19(1): 95-110 (February 2004). DOI: 10.1214/088342304000000017


We review the current state of nonparametric Bayesian inference. The discussion follows a list of important statistical inference problems, including density estimation, regression, survival analysis, hierarchical models and model validation. For each inference problem we review relevant nonparametric Bayesian models and approaches including Dirichlet process (DP) models and variations, Pólya trees, wavelet based models, neural network models, spline regression, CART, dependent DP models and model validation with DP and Pólya tree extensions of parametric models.


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Peter Müller. Fernando A. Quintana. "Nonparametric Bayesian Data Analysis." Statist. Sci. 19 (1) 95 - 110, February 2004.


Published: February 2004
First available in Project Euclid: 14 July 2004

zbMATH: 1057.62032
MathSciNet: MR2082149
Digital Object Identifier: 10.1214/088342304000000017

Keywords: Density estimation , Dirichlet process , Pólya tree , random probability model (RPM) , regression , Survival analysis

Rights: Copyright © 2004 Institute of Mathematical Statistics

Vol.19 • No. 1 • February 2004
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