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Abstract Details
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Activity Number:
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250
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Type:
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Contributed
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Date/Time:
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Monday, July 30, 2012 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #304198 |
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Title:
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Computing Marginal Likelihoods Using Generalized Direct Sampling
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Author(s):
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Michael Braun*+
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Companies:
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MIT Sloan School of Management
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Address:
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One Amherst St, Cambridge, MA, 02142, United States
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Keywords:
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model comparison ;
Bayes factors ;
parallel computation ;
simulation ;
Bayesian estimation ;
hierarchical models
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Abstract:
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Generalized Direct Sampling (GDS) is a method for estimating parameters of high-dimensional non-conjugate hierarchical models. Samples from the posterior distribution are independent, so they can be collected in parallel, with no concern for issues like MCMC chain convergence or autocorrelation. In this talk, I will explain how GDS allows the researcher to estimate the marginal likelihood of the data. The method is stable, fast, generally applicable to a broad class of hierarchical models.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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