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Abstract Details
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Activity Number:
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463
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #302866 |
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Title:
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Locally Optimal Sampler
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Author(s):
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Ting-Li Chen*+ and Shang-Ying Shiu
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Companies:
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Academia Sinica and National Taipei University
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Address:
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Institute of Statistical Science, Taipei, 115, Taiwan
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Keywords:
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Markov chain Monte Carlo ;
Gibbs sampler ;
Metropolis-Hasting sampler ;
asymptotic variance
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Abstract:
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Let A be a finite space and p be an underlying probability on A. For any real-valued function f defined on A, we are interested in calculating the expectation of f under p. Let X1, X2, X3, ... be a Markov chain generated by some transition matrix P with invariant distribution p. The time average, the summation of f(Xk) is a reasonable approximation to the expectation. In this paper, we propose an MCMC algorithm using a locally optimal transition matrix. From our simulation studies, our proposed method outperformed two famous MCMC algorithms, the Gibbs Sampling and the Metropolis-Hastings algorithm.
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