Stochastic differential equation derivation: Comparison of the Markov method versus the additive method

S. Galayda and E. Barany

Physica A: Statistical Mechanics and its Applications, 2012, vol. 391, issue 20, 4564-4574

Abstract: There are several methods of transforming an ordinary differential equation into a stochastic differential equation (SDE). The two most common are adding noise to a system parameter or variable and transforming to a SDE or deriving the SDE by assuming an underlying Markov process. Using simple one- and two-dimensional systems we investigate the differences in dynamics and bifurcations between SDE derived by each method from simple deterministic population models.

Keywords: Stochastic differential equations; Markov process (search for similar items in EconPapers)
Date: 2012
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:391:y:2012:i:20:p:4564-4574

DOI: 10.1016/j.physa.2012.05.028

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