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

被引：43
|
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
收藏
页码：1 / 27
页数：27
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