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An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process

Shah, A and Knowles, DA and Ghahramani, Z (2015) An Empirical Study of Stochastic Variational Algorithms for the Beta Bernoulli Process.

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Abstract

Stochastic variational inference (SVI) is emerging as the most promising candidate for scaling inference in Bayesian probabilistic models to large datasets. However, the performance of these methods has been assessed primarily in the context of Bayesian topic models, particularly latent Dirichlet allocation (LDA). Deriving several new algorithms, and using synthetic, image and genomic datasets, we investigate whether the understanding gleaned from LDA applies in the setting of sparse latent factor models, specifically beta process factor analysis (BPFA). We demonstrate that the big picture is consistent: using Gibbs sampling within SVI to maintain certain posterior dependencies is extremely effective. However, we find that different posterior dependencies are important in BPFA relative to LDA. Particularly, approximations able to model intra-local variable dependence perform best.

Item Type: Article
Uncontrolled Keywords: stat.ML stat.ML cs.LG stat.AP stat.CO stat.ME
Subjects: UNSPECIFIED
Divisions: Div F > Computational and Biological Learning
Depositing User: Cron Job
Date Deposited: 17 Jul 2017 19:34
Last Modified: 16 Nov 2017 02:18
DOI: