Beta-negative binomial process and poisson factor analysis

Mingyuan Zhou, Lauren A. Hannah, David B. Dunson, Lawrence Carin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

61 Scopus citations

Abstract

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.
Original languageEnglish (US)
Title of host publicationJournal of Machine Learning Research
PublisherMicrotome Publishinginfo@mtome.com
Pages1462-1471
Number of pages10
StatePublished - Jan 1 2012
Externally publishedYes

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