A stable manifold MCMC method for high dimensions

被引：7

作者：

Beskos, Alexandros ^{[1
]}

机构：

[1] Natl Univ Singapore, Dept Stat & Appl Probabil, Singapore 117548, Singapore

来源：

STATISTICS & PROBABILITY LETTERS | 2014年 / 90卷

关键词：

Manifold MCMC; Metropolis-adjusted Langevin algorithm; Cameron-Martin space; Infinite dimensions; ALGORITHMS;

D O I：

10.1016/j.spl.2014.03.016

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

We combine two important recent advancements of MCMC algorithms: first, methods utilizing the intrinsic manifold structure of the parameter space; then, algorithms effective for targets in infinite-dimensions with the critical property that their mixing time is robust to mesh refinement. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：46 / 52

页数：7

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