Abstract Details
Activity Number:
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463
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Type:
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Invited
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Date/Time:
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Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #314149
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Title:
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Toward Efficient MCMC for Some High-Dimensional Latent Variable Models
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Author(s):
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Murali Haran*
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Companies:
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Penn State
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Keywords:
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Markov chain Monte Carlo ;
latent variables ;
composite likelihood ;
dimension reduction
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Abstract:
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Among the great successes of Markov chain Monte Carlo (MCMC) methods is their ability to fit latent variable models. In practice, however, if the number of latent variables is large, designing an efficient MCMC algorithm becomes very difficult and MCMC generally becomes prohibitively expensive. I will discuss some approaches for addressing this challenge in latent variable models for spatial data. Among the methods I will describe are reparameterization and dimension-reduction approaches as well as composite likelihood-based methods.
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Authors who are presenting talks have a * after their name.
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