Computational Complexity of Metropolis-Hastings Methods in High Dimensions
Computational Complexity of Metropolis-Hastings Methods in High Dimensions
复制标题
高维 Metropolis-Hastings 方法的计算复杂度
DOI：
10.1007/978-3-642-04107-5_4
复制
发表时间：
2009
影响因子：
8.6
通讯作者：
A. Stuart
中科院分区：
文献类型：
--
作者：
A. Beskos;A. Stuart
本文概述了有关当维度 d 较大时，基于 Metropolis-Hastings 的 MCMC 方法对 ℝ d 进行采样概率测量的计算复杂性的文献。该材料分为三个部分，依次解决以下问题：(i) 对 d 索引的概率度量族做出什么合理假设？ (ii) 关于应用于这些族的 Metropolis-Hastings 方法的计算复杂性，已知什么？ (iii) 该领域还有什么尚未解决的问题？
This article contains an overview of the literature concerning the computational complexity of Metropolis-Hastings based MCMC methods for sampling probability measures on ℝ d , when the dimension d is large. The material is structured in three parts addressing, in turn, the following questions: (i) what are sensible assumptions to make on the family of probability measures indexed by d ? (ii) what is known concerning computational complexity for Metropolis-Hastings methods applied to these families? (iii) what remains open in this area?
影响因子：
4
作者：
Apte, A.;Hairer, M.;Voss, J.
通讯作者：
Voss, J.