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Title: The Multiple-Try Method and Local Optimization in Metropolis Sampling
Authors: Liu, J.S.
Liang, F. 
Wong, W.H.
Keywords: Adaptive direction sampling
Conjugate gradient
Damped sinusoidal
Gibbs sampling
Griddy Gibbs sampler
Hit-and-run algorithm
Markov chain Monte Carlo
Metropolis algorithm
Mixture model
Orientational bias Monte Carlo
Issue Date: Mar-2000
Citation: Liu, J.S.,Liang, F.,Wong, W.H. (2000-03). The Multiple-Try Method and Local Optimization in Metropolis Sampling. Journal of the American Statistical Association 95 (449) : 121-134. ScholarBank@NUS Repository.
Abstract: This article describes a new Metropolis-like transition rule, the multiple-try Metropolis, for Markov chain Monte Carlo (MCMC) simulations. By using this transition rule together with adaptive direction sampling, we propose a novel method for incorporating local optimization steps into a MCMC sampler in continuous state-space. Numerical studies show that the new method performs significantly better than the traditional Metropolis-Hastings (M-H) sampler. With minor tailoring in using the rule, the multiple-try method can also be exploited to achieve the effect of a griddy Gibbs sampler without having to bear with griddy approximations, and the effect of a hit-and-run algorithm without having to figure out the required conditional distribution in a random direction.
Source Title: Journal of the American Statistical Association
ISSN: 01621459
Appears in Collections:Staff Publications

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