Numerical stationary distribution and its convergence for nonlinear stochastic differential equations

Wei Liu, Xuerong Mao

Research output: Contribution to journalArticle

8 Citations (Scopus)
75 Downloads (Pure)

Abstract

To avoid finding the stationary distributions of stochastic differential equations by solving the nontrivial Kolmogorov-Fokker-Planck equations, the numerical stationary distributions are used as the approximations instead. This paper is devoted to approximate the stationary distribution of the underlying equation by the Backward Euler-Maruyama method. Currently existing results [21, 31, 33] are extended in this paper to cover larger range of nonlinear SDEs when the linear growth condition on the drift coeffcient is violated.
Original languageEnglish
Pages (from-to)16-29
Number of pages14
JournalJournal of Computational and Applied Mathematics
Volume276
Early online date27 Aug 2014
DOIs
Publication statusPublished - 1 Mar 2015

Keywords

  • the backward Euler-Maruyama method
  • weak convergence
  • numerical stationary distribution
  • nonlinear SDEs

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