Walker, S.G. (2007) Sampling the Dirichlet mixture model with slices. Communications in Statistics—Simulation and Computation, 36 (1). pp. 45-54. ISSN 0361-0918 .
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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 article, 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.
|Uncontrolled keywords:||Bayesian nonparametrics; density estimation; Dirichlet process; Gibbs sampler; slice sampling|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA276 Mathematical statistics|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Mathematics Statistics and Actuarial Science > Statistics|
|Depositing User:||Maureen Cook|
|Date Deposited:||30 Jun 2008 10:34|
|Last Modified:||14 Jan 2010 14:12|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/3777 (The current URI for this page, for reference purposes)|
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