Statistical Science

Nonparametric Bayesian Data Analysis

Peter Müller and Fernando A. Quintana

Full-text: Open access

Abstract

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.

Article information

Source
Statist. Sci. Volume 19, Number 1 (2004), 95-110.

Dates
First available in Project Euclid: 14 July 2004

Permanent link to this document
http://projecteuclid.org/euclid.ss/1089808275

Digital Object Identifier
doi:10.1214/088342304000000017

Mathematical Reviews number (MathSciNet)
MR2082149

Zentralblatt MATH identifier
1057.62032

Keywords
Dirichlet process regression density estimation survival analysis Pólya tree random probability model (RPM)

Citation

Müller, Peter; Quintana, Fernando A. Nonparametric Bayesian Data Analysis. Statist. Sci. 19 (2004), no. 1, 95--110. doi:10.1214/088342304000000017. http://projecteuclid.org/euclid.ss/1089808275.


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