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Activity Number: 61
Type: Topic Contributed
Date/Time: Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #308683
Title: Parallel Markov Chain Monte Carlo for Nonparametric Mixture Models
Author(s): Sinead Williamson*+
Companies: Carnegie Mellon University
Keywords: Dirichlet process ; MCMC ; parallel inference ; Hierarchical Dirichlet process ; Bayesian nonparametrics
Abstract:

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.


Authors who are presenting talks have a * after their name.

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