Differential Evolution Markov Chain with snooker updater and fewer chains

作     者：ter Braak, Cajo J. F. Vrugt, Jasper A. 

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.