Abstract Details
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Activity Number:
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99
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Type:
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Other
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Date/Time:
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Monday, August 5, 2013 : 8:30 AM to 10:20 AM
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Sponsor:
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ASA
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| Abstract - #307008 |
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Title:
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The Theoretical Underpinnings of MCMC
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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 ;
optimal scalings ;
adaptive MCMC ;
ergodicity
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Abstract:
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Markov chain Monte Carlo (MCMC) algorithms have completely revolutionized Bayesian computation. Underlying this success has been a strong theoretical foundation, which has validated the basic algorithms, provided numerous extensions and generalizations, clarified algorithm options, justified the latest tricks, and evaluated the results. Using simple examples, we will discuss the impact and importance such theoretical MCMC issues as ergodicity, qualitative and quantitative convergence rates, optimal scalings, and adaptive MCMC algorithms.
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Authors who are presenting talks have a * after their name.
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