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Markov Chain Sampling Methods for Dirichlet Process Mixture Models

Radford M. Neal
Journal of Computational and Graphical Statistics
Vol. 9, No. 2 (Jun., 2000), pp. 249-265
DOI: 10.2307/1390653
Stable URL: http://www.jstor.org/stable/1390653
Page Count: 17
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Markov Chain Sampling Methods for Dirichlet Process Mixture Models
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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.

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