Most Probable Phase Portraits of Stochastic Differential Equations and its Numerical Simulation
Probability
2017-03-21 v1
Abstract
A practical and accessible introduction to most probable phase portraits is given. The reader is assumed to be familiar with stochastic differential equations and Euler-Maruyama method in numerical simulation. The article first introduce the method to obtain most probable phase portraits and then give its numerical simulation which is based on Euler-Maruyama method. All of these are given by examples and easy to understand.
Keywords
Cite
@article{arxiv.1703.06789,
title = {Most Probable Phase Portraits of Stochastic Differential Equations and its Numerical Simulation},
author = {Bing Yang and Zhu Zeng and Ling Wang},
journal= {arXiv preprint arXiv:1703.06789},
year = {2017}
}