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Séminaire des équipes de recherche

How accurate is mean-field approximation?

© INRIA Sophie Auvin - R comme Robot

  • Date : 15/12/2016
  • Lieu : Inria de Paris, Bâtiment C, Salle C334
  • Intervenant(s) : Nicolas Gast (INRIA Rhône-Alpes)

Mean-field approximation is a widely used technique to study stochastic systems composed of many interacting objects. It has been successfully  used to analyze the performance of many distributed algorithms, including allocation strategies in server farms, caching algorithms and wireless protocols.

The fundamental idea of mean-field approximation is to study the limiting behavior of the system as the number of interacting objects goes to infinity.

This limiting system is often much easier to study.  In this talk, I will introduce the key concepts behind mean-field approximation, by giving some examples of where it can be applied. I will review some of the classical models and their convergence properties. I will try to answer a very natural question: how large should the system be for mean-field to apply? This will lead to a follow-up question: can we improve this approximation?

Mots-clés : Accurate Mean-field Rap Dyogene Approximation

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