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|Title:||The Multiple-Try Method and Local Optimization in Metropolis Sampling||Authors:||Liu, J.S.
|Keywords:||Adaptive direction sampling
Griddy Gibbs sampler
Markov chain Monte Carlo
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||URI:||http://scholarbank.nus.edu.sg/handle/10635/105425||ISSN:||01621459|
|Appears in Collections:||Staff Publications|
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