Stick-breaking Construction for the Indian Buffet Process

Yee Whye Teh, Dilan Grür, Zoubin Ghahramani
Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, PMLR 2:556-563, 2007.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v2-teh07a, title = {Stick-breaking Construction for the Indian Buffet Process}, author = {Teh, Yee Whye and Grür, Dilan and Ghahramani, Zoubin}, booktitle = {Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics}, pages = {556--563}, year = {2007}, editor = {Meila, Marina and Shen, Xiaotong}, volume = {2}, series = {Proceedings of Machine Learning Research}, address = {San Juan, Puerto Rico}, month = {21--24 Mar}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v2/teh07a/teh07a.pdf}, url = {https://proceedings.mlr.press/v2/teh07a.html}, 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.} }
Endnote
%0 Conference Paper %T Stick-breaking Construction for the Indian Buffet Process %A Yee Whye Teh %A Dilan Grür %A Zoubin Ghahramani %B Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2007 %E Marina Meila %E Xiaotong Shen %F pmlr-v2-teh07a %I PMLR %P 556--563 %U https://proceedings.mlr.press/v2/teh07a.html %V 2 %X 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.
RIS
TY - CPAPER TI - Stick-breaking Construction for the Indian Buffet Process AU - Yee Whye Teh AU - Dilan Grür AU - Zoubin Ghahramani BT - Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics DA - 2007/03/11 ED - Marina Meila ED - Xiaotong Shen ID - pmlr-v2-teh07a PB - PMLR DP - Proceedings of Machine Learning Research VL - 2 SP - 556 EP - 563 L1 - http://proceedings.mlr.press/v2/teh07a/teh07a.pdf UR - https://proceedings.mlr.press/v2/teh07a.html AB - 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. ER -
APA
Teh, Y.W., Grür, D. & Ghahramani, Z.. (2007). Stick-breaking Construction for the Indian Buffet Process. Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 2:556-563 Available from https://proceedings.mlr.press/v2/teh07a.html.

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