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作者机构:Wageningen Univ & Res Ctr NL-6700 AC Wageningen Netherlands Los Alamos Natl Lab CNLS Los Alamos NM 87545 USA
出 版 物:《STATISTICS AND COMPUTING》 (统计学与计算)
年 卷 期:2008年第18卷第4期
页 面:435-446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)] 0812[工学-计算机科学与技术(可授工学、理学学位)]
基 金:Los Alamos National Laboratory LANL
主 题:Evolutionary Monte Carlo Metropolis algorithm Adaptive Markov chain Monte Carlo Theophylline kinetics Adaptive direction sampling Parallel computing Differential evolution
摘 要:Differential Evolution Markov Chain (DE-MC) is an adaptive MCMC algorithm, in which multiple chains are run in parallel. Standard DE-MC requires at least N=2d chains to be run in parallel, where d is the dimensionality of the posterior. This paper extends DE-MC with a snooker updater and shows by simulation and real examples that DE-MC can work for d up to 50-100 with fewer parallel chains (e.g. N=3) by exploiting information from their past by generating jumps from differences of pairs of past states. This approach extends the practical applicability of DE-MC and is shown to be about 5-26 times more efficient than the optimal Normal random walk Metropolis sampler for the 97.5% point of a variable from a 25-50 dimensional Student t (3) distribution. In a nonlinear mixed effects model example the approach outperformed a block-updater geared to the specific features of the model.