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Abstract Details
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Activity Number:
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47
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Type:
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Invited
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Date/Time:
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Sunday, July 29, 2012 : 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 - #303636 |
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Title:
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Self-Learning Markov Chain Monte Carlo from a Large Deviations Point of View
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Author(s):
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Jingchen Liu*+ and Xuan Yang and Henrik Hult
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Companies:
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Columbia University and Columbia University and Royal Institute of Technology, Sweden
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Address:
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1255 Amsterdam Avenue, New York, NY, , United States
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Keywords:
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MCMC ;
large deviations ;
Adaptive Monte Carlo
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Abstract:
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Measuring the convergence of Markov chains and comparing the efficiency among different MCMC schemes have been a long lasting problem. In this talk, we propose using the large deviations rate function as an efficiency measure of an MCMC scheme. This measure turns out to be consistent with the existing asymptotic results in literature. In addition, the proposed efficiency measure can be evaluated numerically by the Markov chain itself and thus can be combined with adaptive MCMC schemes.
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Authors who are presenting talks have a * after their name.
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