Numerical method for stationary distribution of stochastic differential equations with Markovian switching

被引：46

作者：

Mao, XR

Yuan, CG

Yin, G

机构：

[1] Univ Strathclyde, Dept Stat & Modelling Sci, Glasgow G1 1XH, Lanark, Scotland

[2] Wayne State Univ, Dept Math, Detroit, MI 48202 USA

来源：

JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS | 2005年 / 174卷 / 01期

基金：

美国国家科学基金会;

关键词：

Brownian motion; Stationary distribution; Lipschitz condition; Markov chain; stochastic differential equations; Euler-Maruyama methods; weak convergence to stationary measures;

D O I：

10.1016/j.cam.2004.03.016

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

In principle, once the existence of the stationary distribution of a stochastic differential equation with Markovian switching is assured, we may compute it by solving the associated system of the coupled Kolmogorov-Fokker-Planck equations. However, this is nontrivial in practice. As a viable alternative, we use the Euler-Maruyama scheme to obtain the stationary distribution in this paper. (C) 2004 Elsevier B.V. All rights reserved.

引用

页码：1 / 27

页数：27

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[1] COMMAND AND CONTROL (C2) THEORY - A CHALLENGE TO CONTROL SCIENCE [J].