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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 - #303576 |
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Title:
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Interacting Multiple-Try Metropolis Sampling
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Author(s):
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Radu V Craiu*+ and Roberto Casarin and Fabrizio Leisen
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Companies:
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University of Toronto and University of Venice and Universidad Carlos III de Madrid
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Address:
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100 St George Street , Toronto, ON, M5S3G3, Canada
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Keywords:
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Adaptive MCMC ;
Interacting MCMC ;
Multiple-try Metropolis ;
Population Monte Carlo ;
Simulated Annealing
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Abstract:
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The Multiple-try Metropolis (MTM) algorithm of Liu, Liang and Wong (JASA, 2000) generalizes the classical Metropolis-Hastings algorithm as the next state of the chain is selected from a set of candidates sampled from the proposal distribution. We show that a simple modification of the original MTM allows each candidate to be sampled from a different proposal distribution. This additional flexibility allows us to introduce a novel interacting MTM mechanism (IMTM) that expands the class of population-based Monte Carlo methods and builds connections with the rapidly expanding world of adaptive MCMC. We discuss tuning of the simulation parameters and illustrate the performance of the method with numerical examples.
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