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  Stick-breaking Construction for the Indian Buffet Process

Teh, Y., Görür, D., & Ghahramani, Z. (2007). Stick-breaking Construction for the Indian Buffet Process. In M. Meila, & X. Shen (Eds.), Artificial Intelligence and Statistics, 21-24 March 2007, San Juan, Puerto Rico (pp. 556-563). Madison, WI, USA: International Machine Learning Society.

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 Creators:
Teh, YW, Author
Görür, D1, 2, Author           
Ghahramani, Z, Author
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              

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 Abstract: The Indian buffet process (IBP) is a Bayesian nonparametric distribution whereby objects are modelled using an unbounded number of latent features. In this paper we derive a stick-breaking representation for the IBP. Based on this new representation, we develop slice samplers for the IBP that are efficient, easy to implement and are more generally applicable than the currently available Gibbs sampler. This representation, along with the work of Thibaux and Jordan [17], also illuminates interesting theoretical connections between the IBP, Chinese restaurant processes, Beta processes and Dirichlet processes.

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 Dates: 2007-03
 Publication Status: Published in print
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 Identifiers: URI: http://jmlr.csail.mit.edu/proceedings/papers/v2/teh07a.html
BibTex Citekey: 5361
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Title: 11th International Conference on Artificial Intelligence and Statistics (AISTATS 2007)
Place of Event: San Juan, Puerto Rico
Start-/End Date: 2007-03-21 - 2007-03-24

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Title: Artificial Intelligence and Statistics, 21-24 March 2007, San Juan, Puerto Rico
Source Genre: Proceedings
 Creator(s):
Meila, M, Editor
Shen, X, Editor
Affiliations:
-
Publ. Info: Madison, WI, USA : International Machine Learning Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 556 - 563 Identifier: -

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Title: JMLR Workshop and Conference Proceedings
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Pages: - Volume / Issue: 2 Sequence Number: - Start / End Page: - Identifier: -