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arXiv · 1307.6769

Streaming Variational Bayes

Tamara Broderick·Nicholas Boyd·Andre Wibisono·Ashia C. Wilson·Michael I. Jordan
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Abstract

We present SDA-Bayes, a framework for (S)treaming, (D)istributed, (A)synchronous computation of a Bayesian posterior. The framework makes streaming updates to the estimated posterior according to a user-specified approximation batch primitive. We demonstrate the usefulness of our framework, with variational Bayes (VB) as the primitive, by fitting the latent Dirichlet allocation model to two large-scale document collections. We demonstrate the advantages of our algorithm over stochastic variational inference (SVI) by comparing the two after a single pass through a known amount of data---a case where SVI may be applied---and in the streaming setting, where SVI does not apply.

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Tamara Broderick, Nicholas Boyd, Andre Wibisono, Ashia C. Wilson, Michael I. Jordan. 2013-11-20. Streaming Variational Bayes. https://arxiv.org/abs/1307.6769

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