Self-stabilizing processes: uniqueness problem for stationary measures and convergence rate in the small noise limit

Samuel Herrmann (1, 2) , Julian Tugaut (1, 2)

1 IECN - Institut Élie Cartan de Nancy
2 TOSCA - Simuler et calibrer des modèles stochastiques

In the context of self-stabilizing processes, that is processes attracted by their own law, leaving in some potential landscape, we investigate different properties of the invariant measures. The interaction between the process and its law leads to nonlinear stochastic differential equations. In some previous work, the authors proved that, for linear interaction and under suitable conditions, there exist some unique symmetric limit measure associated to the set of invariant measures in the small noise limit. The aim of this study is essentially to point out that this statement leads to the existence, as the noise intensity is small, of one unique symmetric invariant measure for the self-stabilizing process. Informations about the asymmetric measures shall be presented too. The main key consists in estimating the convergence rate for sequences of stationary measures using generalized Laplace's method approximations.

Mots clés: self-interacting diffusion; McKean-Vlasov equation; stationary measures; double well potential; perturbed dynamical system; Laplace's method; fixed point theorem; uniqueness problem

Domaines: Probabilités [math.PR]

Samuel Herrmann, Julian Tugaut. Self-stabilizing processes: uniqueness problem for stationary measures and convergence rate in the small noise limit. 2009. ⟨hal-00434254⟩