Sampling the Dirichlet Mixture Model with Slices
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.
|Date of creation:||Jul 2006|
|Contact details of provider:|| Postal: Corso Unione Sovietica, 218bis - 10134 Torino - Italy|
Phone: +39 011 6706060
Fax: +39 011 6706062
Web page: http://www.esomas.unito.it/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- 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.
When requesting a correction, please mention this item's handle: RePEc:icr:wpmath:16-2006. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Simone Pellegrino)
If references are entirely missing, you can add them using this form.