Weak convergence of metropolis algorithms for non-IID target distributions
成果类型：
Article
署名作者：
Bedard, Mylene
署名单位：
University of Warwick
刊物名称：
ANNALS OF APPLIED PROBABILITY
ISSN/ISSBN：
1050-5164
DOI：
10.1214/105051607000000096
发表日期：
2007
页码：
1222-1244
关键词：
摘要：
In this paper, we shall optimize the efficiency of Metropolis algorithms for multidimensional target distributions with scaling terms possibly depending on the dimension. We propose a method for determining the appropriate form for the scaling of the proposal distribution as a function of the dimension, which leads to the proof of an asymptotic diffusion theorem. We show that when there does not exist any component with a scaling term significantly smaller than the others, the asymptotically optimal acceptance rate is the well-known 0.234.