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2009 Mean field inference for the Dirichlet process mixture model
O. Zobay
Electron. J. Statist. 3: 507-545 (2009). DOI: 10.1214/08-EJS339


We present a systematic study of several recently proposed methods of mean field inference for the Dirichlet process mixture (DPM) model. These methods provide approximations to the posterior distribution and are derived using the truncated stick-breaking representation and related approaches. We investigate their use in density estimation and cluster allocation and compare to Monte-Carlo results. Further, more specific topics include the general mathematical structure of the mean field approximation, the handling of the truncation level, the effect of including a prior on the concentration parameter α of the DPM model, the relationship between the proposed variants of the mean field approximation, and the connection to maximum a-posteriori estimation of the DPM model.


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O. Zobay. "Mean field inference for the Dirichlet process mixture model." Electron. J. Statist. 3 507 - 545, 2009.


Published: 2009
First available in Project Euclid: 11 June 2009

zbMATH: 1326.62035
MathSciNet: MR2519531
Digital Object Identifier: 10.1214/08-EJS339

Primary: 62E17
Secondary: 62G07

Keywords: approximation methods , Bayesian nonparametrics , Density estimation , variational inference

Rights: Copyright © 2009 The Institute of Mathematical Statistics and the Bernoulli Society


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