The multiple-try method and local optimization in metropolis sampling

作     者：Liu, JS Liang, FM Wong, WH 

作者机构：Stanford Univ Dept Stat Stanford CA 94305 USA Univ Calif Los Angeles Dept Stat Los Angeles CA 90095 USA 

出 版 物：《JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION》 (J. Am. Stat. Assoc.)

年 卷 期：2000年第95卷第449期

页      面：121-134页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

主　　题：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 

摘      要：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.