Abstract Details
Activity Number:
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61
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Type:
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Topic Contributed
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Date/Time:
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Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract - #308683 |
Title:
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Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models
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Author(s):
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Sinead Williamson*+
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Companies:
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Carnegie Mellon University
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Keywords:
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Dirichlet process ;
MCMC ;
parallel inference ;
Hierarchical Dirichlet process ;
Bayesian nonparametrics
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Abstract:
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Nonparametric mixture models based on the Dirichlet process are an elegant alternative to finite models when the number of underlying components is unknown, but inference in such models can be slow. Existing attempts to parallelize inference in such models have relied on introducing approximations, which can lead to inaccuracies in the posterior estimate. In this talk, I will construct auxiliary variable representations for the Dirichlet process and the hierarchical Dirichlet process that facilitate the development of distributed Markov chain Monte Carlo schemes that use the correct equilibrium distribution. Experimental analyses show that this approach allows scalable inference without the deterioration in estimate quality that accompanies existing methods.
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Authors who are presenting talks have a * after their name.
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