Symmetrized importance samplers for stochastic differential equations

Andrew Leach, Kevin K. Lin, Matthias Morzfeld

Research output: Contribution to journalArticlepeer-review


We study a class of importance sampling methods for stochastic differential equations (SDEs). A small noise analysis is performed, and the results suggest that a simple symmetrization procedure can significantly improve the performance of our importance sampling schemes when the noise is not too large. We demonstrate that this is indeed the case for a number of linear and nonlinear examples. Potential applications, e.g., data assimilation, are discussed.

Original languageEnglish (US)
Pages (from-to)215-241
Number of pages27
JournalCommunications in Applied Mathematics and Computational Science
Issue number2
StatePublished - 2018


  • Data assimilation
  • Importance sampling
  • Small noise theory
  • Stochastic differential equations
  • Symmetrization

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Applied Mathematics


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