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Markov Chain Sampling Methods for Dirichlet Process Mixture Models

Markov Chain Sampling Methods for Dirichlet Process Mixture Models Abstract This article reviews Markov chain methods for sampling from the posterior distribution of a Dirichlet process mixture model and presents two new classes of methods. One new approach is to make Metropolis—Hastings updates of the indicators specifying which mixture component is associated with each observation, perhaps supplemented with a partial form of Gibbs sampling. The other new approach extends Gibbs sampling for these indicators by using a set of auxiliary parameters. These methods are simple to implement and are more efficient than previous ways of handling general Dirichlet process mixture models with non-conjugate priors. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Computational and Graphical Statistics Taylor & Francis

Markov Chain Sampling Methods for Dirichlet Process Mixture Models

17 pages

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References (22)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
1537-2715
eISSN
1061-8600
DOI
10.1080/10618600.2000.10474879
Publisher site
See Article on Publisher Site

Abstract

Abstract This article reviews Markov chain methods for sampling from the posterior distribution of a Dirichlet process mixture model and presents two new classes of methods. One new approach is to make Metropolis—Hastings updates of the indicators specifying which mixture component is associated with each observation, perhaps supplemented with a partial form of Gibbs sampling. The other new approach extends Gibbs sampling for these indicators by using a set of auxiliary parameters. These methods are simple to implement and are more efficient than previous ways of handling general Dirichlet process mixture models with non-conjugate priors.

Journal

Journal of Computational and Graphical StatisticsTaylor & Francis

Published: Jun 1, 2000

Keywords: Auxiliary variable methods; Density estimation; Latent class models; Monte Carlo; Metropolis—Hasting algorithm

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