Variational inference for stick-breaking beta process priors

John Paisley; Lawrence Carin; David Blei

Variational inference for stick-breaking beta process priors

John Paisley, Lawrence Carin, David Blei

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We present a variational Bayesian inference algorithm for the stick-breaking construction of the beta process. We derive an alternate representation of the beta process that is amenable to variational inference, and present a bound relating the truncated beta process to its infinite counterpart. We assess performance on two matrix factorization problems, using a non-negative factorization model and a linear-Gaussian model. Copyright 2011 by the author(s)/owner(s).

Original language	English (US)
Title of host publication	Proceedings of the 28th International Conference on Machine Learning, ICML 2011
Pages	889-896
Number of pages	8
State	Published - Oct 7 2011
Externally published	Yes

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Generated from Scopus record by KAUST IRTS on 2021-02-09

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title = "Variational inference for stick-breaking beta process priors",

abstract = "We present a variational Bayesian inference algorithm for the stick-breaking construction of the beta process. We derive an alternate representation of the beta process that is amenable to variational inference, and present a bound relating the truncated beta process to its infinite counterpart. We assess performance on two matrix factorization problems, using a non-negative factorization model and a linear-Gaussian model. Copyright 2011 by the author(s)/owner(s).",

author = "John Paisley and Lawrence Carin and David Blei",

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year = "2011",

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AU - Carin, Lawrence

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PY - 2011/10/7

Y1 - 2011/10/7

N2 - We present a variational Bayesian inference algorithm for the stick-breaking construction of the beta process. We derive an alternate representation of the beta process that is amenable to variational inference, and present a bound relating the truncated beta process to its infinite counterpart. We assess performance on two matrix factorization problems, using a non-negative factorization model and a linear-Gaussian model. Copyright 2011 by the author(s)/owner(s).

AB - We present a variational Bayesian inference algorithm for the stick-breaking construction of the beta process. We derive an alternate representation of the beta process that is amenable to variational inference, and present a bound relating the truncated beta process to its infinite counterpart. We assess performance on two matrix factorization problems, using a non-negative factorization model and a linear-Gaussian model. Copyright 2011 by the author(s)/owner(s).

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M3 - Conference contribution

SN - 9781450306195

SP - 889

EP - 896

BT - Proceedings of the 28th International Conference on Machine Learning, ICML 2011

ER -

Variational inference for stick-breaking beta process priors

Abstract

Bibliographical note

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