Abstract Details
Activity Number:
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154
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 4, 2014 : 10:30 AM to 12:20 PM
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Sponsor:
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International Society for Bayesian Analysis (ISBA)
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Abstract #312070
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View Presentation
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Title:
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SDA-Bayes and MAD-Bayes for Large-Scale Bayesian Analysis
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Author(s):
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Michael Jordan*+
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Companies:
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University of California, Berkeley
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Keywords:
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variational methods ;
streaming data ;
distributed computing ;
Bayesian computation
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Abstract:
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We present two lines of research aimed at Bayesian analysis of very large data sets. The first, SDA-Bayes, is a framework for (S)treaming, (D)istributed, (A)synchronous computation of a Bayesian posterior. The framework makes streaming updates to an approximate posterior according to a user-specified approximation batch primitive. We demonstrate the usefulness of our framework, with variational Bayes as the primitive, by fitting the latent Dirichlet allocation model to two large-scale document collections. The second, MAD-Bayes, is based on taking a small-variance asymptotic expansion of the posterior, yielding fast deterministic algorithms that are in the spirit of the classical K-means algorithm for clustering, but taking advantage of the flexibility of Bayesian nonparametric priors. [Joint work with Nick Boyd, Tamara Broderick, Brian Kulis, Andre Wibisono and Ashia Wilson.]
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Authors who are presenting talks have a * after their name.
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