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Stick-breaking construction for the Indian buffet process

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 intere...

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Journal:
Journal of Machine Learning Research
Volume:
2
Pages:
556-563
Publication date:
2007-01-01
EISSN:
1533-7928
ISSN:
1532-4435
URN:
uuid:6e65f1e3-6181-4cb3-9474-bd4ae391cd0f
Source identifiers:
353264
Local pid:
pubs:353264
Language:
English

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