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Activity Number:
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460
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 1, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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| Abstract - #309293 |
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Title:
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Improving SAMC Using Smoothing Methods: Theory and Applications
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Author(s):
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Faming Liang*+
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Companies:
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Texas A&M University
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Address:
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Department of Statistics, College Station, TX, 77843,
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Keywords:
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Model selection ; Stochastic Approximation ; smoothing ; Monte Carlo
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Abstract:
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Stochastic approximation Monte Carlo (SAMC) has recently been proposed by Liang et al (2006) as a general simulation and optimization algorithm. In this talk, we propose to improve its convergence using smoothing methods and discuss the application of the new algorithm to Bayesian model selection problems. Our numerical results show that the improvement is significant. The new algorithm represents a general form of the stochastic approximation Markov chain Monte Carlo algorithm. It allows multiple samples to be generated at each iteration, and a bias term to be included in the parameter updating step. A rigorous proof for the convergence of the general algorithm is established under verifiable conditions. This paper also provides a framework on how to incorporate nonparametric techniques into Monte Carlo methods to improve Bayesian computation.
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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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