Sampling the Dirichlet Mixture Model with Slices
AbstractWe 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.
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Bibliographic InfoPaper provided by ICER - International Centre for Economic Research in its series ICER Working Papers - Applied Mathematics Series with number 16-2006.
Length: 18 pages
Date of creation: Jul 2006
Date of revision:
Bayesian Nonparametrics; Density estimation; Dirich-let process; Gibbs sampler; Slice sampling.;
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- P. Damlen & J. Wakefield & S. Walker, 1999. "Gibbs sampling for Bayesian non-conjugate and hierarchical models by using auxiliary variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 331-344.
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