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Gibbs Sampling Methods for Stick-Breaking Priors
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@MISC{Ishwaran_gibbssampling,
author = {Hemant Ishwaran and Lancelot F. James},
title = {Gibbs Sampling Methods for Stick-Breaking Priors},
year = {}
}
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Abstract
... In this paper we present two general types of Gibbs samplers that can be used to fit posteriors of Bayesian hierarchical models based on stick-breaking priors. The first type of Gibbs sampler, referred to as a Polya urn Gibbs sampler, is a generalized version of a widely used Gibbs sampling method currently employed for Dirichlet process computing. This method applies to stick-breaking priors with a known P'olya urn characterization; that is priors with an explicit and simple prediction rule. Our second method, the blocked Gibbs sampler, is based on a entirely different approach that works by directly sampling values from the posterior of the random measure. The blocked Gibbs sampler can be viewed as a more general approach as it works without requiring an explicit prediction rule. We find that the blocked Gibbs avoids some of the limitations seen with the Polya urn approach and should be simpler for non-experts to use.
Keyphrases
stick-breaking prior gibbs sampling method blocked gibbs sampler gibbs sampler second method simple prediction rule first type generalized version fit posterior dirichlet process polya urn gibbs sampler different approach general type explicit prediction rule olya urn characterization blocked gibbs polya urn approach bayesian hierarchical model random measure general approach
