Beta-negative binomial process and poisson factor analysis

dc.contributor.author

Zhou, M

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Hannah, LA

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Dunson, DB

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Carin, L

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Lawrence, Neil D

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Girolami, Mark A

dc.date.accessioned

2017-10-01T21:16:59Z

dc.date.available

2017-10-01T21:16:59Z

dc.date.issued

2012-01-01

dc.description.abstract

© Copyright 2012 by the authors. A beta-negative binomial (BNB) process is proposed, leading to a beta-gamma-Poisson process, which may be viewed as a "multiscoop" generalization of the beta-Bernoulli process. The BNB process is augmented into a beta-gamma-gamma-Poisson hierarchical structure, and applied as a nonparametric Bayesian prior for an infinite Poisson factor analysis model. A finite approximation for the beta process Lévy random measure is constructed for convenient implementation. Efficient MCMC computations are performed with data augmentation and marginalization techniques. Encouraging results are shown on document count matrix factorization.

dc.identifier.eissn

1533-7928

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1532-4435

dc.identifier.uri

https://hdl.handle.net/10161/15597

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JMLR.org

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Journal of Machine Learning Research

dc.title

Beta-negative binomial process and poisson factor analysis

dc.type

Journal article

pubs.begin-page

1462

pubs.end-page

1471

pubs.organisational-group

Duke

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Duke Clinical Research Institute

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Duke Institute for Brain Sciences

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Electrical and Computer Engineering

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Institutes and Centers

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Institutes and Provost's Academic Units

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Pratt School of Engineering

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School of Medicine

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Statistical Science

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Trinity College of Arts & Sciences

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University Institutes and Centers

pubs.publication-status

Published

pubs.volume

22

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