Sensitivity of output of a linear operator to its input can be quantified in various ways. In Control Theory, the input is usually interpreted as disturbance and the output is to be minimized in some sense. In stochastic worst-case design settings, the disturbance is considered random with imprecisely known probability distribution. The prior set of probability measures can be chosen so as to quantify how far the disturbance deviates from the white-noise hypothesis of Linear Quadratic Gaussian control. Such deviation can be measured by the minimal Kullback-Leibler informational divergence from the Gaussian distributions with zero mean and scalar covariance matrices. The resulting anisotropy functional is defined for finite power random vect...
We develop a new method for deriving local laws for a large class of random matrices. It is applicab...
We obtain formulae for the expected number and height distribution of critical points of smooth isot...
Let X = {X(t), t ∈ RN} be a Gaussian random field with values in Rd defined by X(t) = X1(t),..., Xd(...
Sensitivity of output of a linear operator to its input can be quantified in various ways. In Contro...
Sensitivity of output of a linear operator to its input can be quantified in various ways. In Contro...
Special Issue: Proceedings of the "XIème Colloque Franco-Roumain de Mathématiques Appliquées"Interna...
We study the sample paths properties of Operator scaling Gaussian random fields. Such fields are ani...
Anisotropic Gaussian random fields arise in probability theory and in various applications. Typical ...
Summarization: Spatially referenced data often have autocovariance functions with elliptical isoleve...
We consider a problem of robust performance analysis of linear discrete time varying systems on a bo...
This paper studies polar sets of anisotropic Gaussian random fields, i.e. sets which a Gaussian rand...
This paper studies polar sets for anisotropic Gaussian random fields, i.e. sets which a Gaussian ran...
Thèse réalisée au laboratoire MAP5 de l'université Paris Descartes.This thesis deals with anisotropi...
International audienceThe characterization and estimation of the Hölder regularity of random fields ...
The paper states a problem and considers two possible methods to estimate the anisotropic norm upper...
We develop a new method for deriving local laws for a large class of random matrices. It is applicab...
We obtain formulae for the expected number and height distribution of critical points of smooth isot...
Let X = {X(t), t ∈ RN} be a Gaussian random field with values in Rd defined by X(t) = X1(t),..., Xd(...
Sensitivity of output of a linear operator to its input can be quantified in various ways. In Contro...
Sensitivity of output of a linear operator to its input can be quantified in various ways. In Contro...
Special Issue: Proceedings of the "XIème Colloque Franco-Roumain de Mathématiques Appliquées"Interna...
We study the sample paths properties of Operator scaling Gaussian random fields. Such fields are ani...
Anisotropic Gaussian random fields arise in probability theory and in various applications. Typical ...
Summarization: Spatially referenced data often have autocovariance functions with elliptical isoleve...
We consider a problem of robust performance analysis of linear discrete time varying systems on a bo...
This paper studies polar sets of anisotropic Gaussian random fields, i.e. sets which a Gaussian rand...
This paper studies polar sets for anisotropic Gaussian random fields, i.e. sets which a Gaussian ran...
Thèse réalisée au laboratoire MAP5 de l'université Paris Descartes.This thesis deals with anisotropi...
International audienceThe characterization and estimation of the Hölder regularity of random fields ...
The paper states a problem and considers two possible methods to estimate the anisotropic norm upper...
We develop a new method for deriving local laws for a large class of random matrices. It is applicab...
We obtain formulae for the expected number and height distribution of critical points of smooth isot...
Let X = {X(t), t ∈ RN} be a Gaussian random field with values in Rd defined by X(t) = X1(t),..., Xd(...