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Stick-breaking Construction for the Indian Buffet Process

2007

Conference Paper

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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.

Author(s): Teh, YW. and Görür, D. and Ghahramani, Z.
Book Title: JMLR Workshop and Conference Proceedings Volume 2: AISTATS 2007
Journal: Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS 2007)
Pages: 556-563
Year: 2007
Month: March
Day: 0
Editors: Meila, M. , X. Shen
Publisher: MIT Press

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

Event Name: 11th International Conference on Artificial Intelligence and Statistics
Event Place: San Juan, Puerto Rico

Address: Cambridge, MA, USA
Digital: 0
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF
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BibTex

@inproceedings{5361,
  title = {Stick-breaking Construction for the Indian Buffet Process},
  author = {Teh, YW. and G{\"o}r{\"u}r, D. and Ghahramani, Z.},
  journal = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS 2007)},
  booktitle = {JMLR Workshop and Conference Proceedings Volume 2: AISTATS 2007},
  pages = {556-563},
  editors = {Meila, M. , X. Shen},
  publisher = {MIT Press},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Cambridge, MA, USA},
  month = mar,
  year = {2007},
  month_numeric = {3}
}