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Abstract Details
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Activity Number:
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613
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 4, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #303003 |
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Title:
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The Generalized Multiset Sampler
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Author(s):
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Hang J. Kim*+ and Steven N. MacEachern
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Companies:
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The Ohio State University and The Ohio State University
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Address:
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1958 Neil Avenue, Cockins Hall Room 404, Columbus, OH, 43210, United States
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Keywords:
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Multiset sampler ;
MCMC ;
Multimodal ;
Metropolis algorithm ;
Poor mixing ;
Mixture model
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Abstract:
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The multiset sampler (MSS) proposed by Leman et al. (2009) is a new MCMC algorithm, especially useful to draw samples from a multimodal distribution, and easy to implement. We generalize the algorithm by redefining the MSS with explicit description of the link between target distribution and sampling distribution. The generalized formulation makes the idea of multiset (or k-tuple) applicable not only to Metropolis-Hastings, but also to other sampling methods. The basic properties of sampling distribution are provided. Drawing on results from importance sampling, we also create effective estimators for both the basic multiset sampler and the generalization we propose. Simulation examples confirm that the generalized multiset sampler (GMSS) is a general and easy approach to deal with multimodality and to produce a chain that mixes well.
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