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
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
相关论文
共 50 条