Simplified formulas for the mean and variance of linear stochastic differential equations

DOI10.1016/j.aml.2015.04.009zbMath1381.60093arXiv1207.5067OpenAlexW2962722979MaRDI QIDQ289367

F. Blanchet-Sadri, M. Dambrine

Publication date: 30 May 2016

Published in: Applied Mathematics Letters (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1207.5067

zbMATH Keywords

system identificationstochastic differential equationsdiffusion processlocal linearization

Mathematics Subject Classification ID

Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10) Ordinary differential equations and systems with randomness (34F05)

Related Items (4)

A weak local linearization scheme for stochastic differential equations with multiplicative noise ⋮ Estimation of distribution algorithms for the computation of innovation estimators of diffusion processes ⋮ Controlling roughening processes in the stochastic Kuramoto-Sivashinsky equation ⋮ Computing high dimensional multiple integrals involving matrix exponentials

Uses Software

• Expokit
• MATLAB expm

Cites Work

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• Local linearization filters for non-linear continuous-discrete state space models with multiplicative noise
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• The Scaling and Squaring Method for the Matrix Exponential Revisited

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