Sampling the Dirichlet Mixture Model with Slices

Stephen G. Walker ()

ICER Working Papers - Applied Mathematics Series from ICER - International Centre for Economic Research

Abstract: We provide a new approach to the sampling of the well known mixture of Dirichlet process model. Recent attention has focused on retention of the random distribution function in the model, but sampling algorithms have then suffered from the countably infinite representation these distributions have. The key to the algorithm detailed in this paper, which also keeps the random distribution functions, is the introduction of a latent variable which allows a finite number, which is known, of objects to be sampled within each iteration of a Gibbs sampler.

Keywords: Bayesian Nonparametrics; Density estimation; Dirich-let process; Gibbs sampler; Slice sampling. (search for similar items in EconPapers)
Pages: 18 pages
Date: 2006-07
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Persistent link: https://EconPapers.repec.org/RePEc:icr:wpmath:16-2006

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