Abstract Details
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Activity Number:
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417
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #309127 |
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Title:
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Comparing the Efficiency of Adaptive MCMC Algorithms
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Author(s):
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Scott C. Schmidler*+
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Companies:
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Duke University
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Keywords:
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MCMC ;
Bayesian ;
mixing times ;
adaptive ;
Monte Carlo
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Abstract:
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We summarize some recent results on the convergence rates of adaptive MCMC algorithms for Bayesian inference. We consider the challenges in obtaining theoretical bounds on convergence rates, describe some lower bounds on mixing times for several popular adaptation schemes, and present examples from Bayesian exponential regression and model selection. We show how this analysis provides insight that leads directly to development of new, improved adaptive sampling algorithms.
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Authors who are presenting talks have a * after their name.
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