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A Refined Mean Field Approximation for Synchronous Population Processes

Abstract : Mean field approximation is a popular method to study the behaviour of stochastic models composed of a large number of interacting objects. When the objects are asynchronous, the mean field approximation of a population model can be expressed as an ordinary differential equation. When the objects are synchronous the mean field approximation is a discrete time dynamical system. In this paper, we focus on the latter. We show that, similarly to the asynchronous case, the mean field approximation of a synchronous population can be refined by a term in 1/N. Our result holds for finite time-horizon and steady-state. We provide two examples that illustrate the approach and its limit.
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Contributor : Nicolas Gast Connect in order to contact the contributor
Submitted on : Tuesday, October 9, 2018 - 5:59:10 PM
Last modification on : Wednesday, July 6, 2022 - 4:16:28 AM


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  • HAL Id : hal-01891629, version 1


Nicolas Gast, Diego Latella, Mieke Massink. A Refined Mean Field Approximation for Synchronous Population Processes. MAMA 2018Workshop on MAthematical performance Modeling and Analysis, Jun 2018, Irvine, United States. pp.1-3. ⟨hal-01891629⟩



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