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.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
page. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
Length: 18 pages Date of creation: Jul 2006 Date of revision: Handle: RePEc:icr:wpmath:16-2006
Contact details of provider: Postal: Viale Settimio Severo, 63 - 10133 Torino - Italy Phone: +39 011 6604828 Fax: +39 011 6600082 Email: Web page: http://www.icer.it More information through EDIRC
For technical questions regarding this item, or to correct its listing, contact: (Alessandra Calosso).
References listed on IDEAS 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.: