Abstract Details
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Activity Number:
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324
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Type:
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Invited
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Date/Time:
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Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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| Abstract - #307007 |
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Title:
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Ergodicity of Adaptive MCMC Algorithms
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Author(s):
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Jeffrey S. Rosenthal*+
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Companies:
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University of Toronto
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Keywords:
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MCMC ;
Markov chain Monte Carlo ;
Markov chains ;
convergence ;
adaptive MCMC ;
ergodicity
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Abstract:
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Adaptive MCMC holds great promise to let computers automatically discover good Markov chains. Unfortunately, it is well-known that even natural-seeming adaptive schemes can destroy the ergodicity properties necessary for MCMC to be valid. On the other hand, there are many recent papers ensuring ergodicity of adaptive MCMC under various conditions. In this talk, we will review adaptive MCMC algorithms, and discuss cases in which they will or will not converge to stationarity. We will also present certain general-purpose adaptive MCMC algorithms, some of which have shown very promising results in specific examples and applications.
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Authors who are presenting talks have a * after their name.
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