We consider a random map x → F (ω, x) and a random variable Θ(ω), and we denote by F^N (ω, x) and Θ^N (ω) their approximations: We establish a strong convergence result, in Lp-norms, of the compound approximation F^N (ω, Θ^N (ω)) to the compound variable F (ω, Θ(ω)), in terms of the approximations of F and Θ. We then apply this result to the composition of two Stochastic Differential Equations through their initial conditions, which can give a way to solve some Stochastic Partial Differential Equations
The paper studies stochastic optimization (programming) problems with compound functions containing ...
Let (Xn)n[epsilon] be a sequence of real, independent, not necessarily identically distributed rando...
We generalize the results of Komlós, Major and Tusnády concerning the strong approximation of parti...
We consider a random map x → F (ω, x) and a random variable Θ(ω), and we denote by F^N (ω, x) and Θ^...
We show that if a sequence of piecewise affine linear processes converges in the strong sense with a...
International audienceWe study the convergence rates of strong approximations of stochastic processe...
his talk is devoted to strong approximations in the dependent setting. The famous results of Koml\'o...
AbstractIn this note we consider the almost sure convergence (as ϵ→0) of solution Xϵ(·), defined ove...
This thesis is concerned with numerical approximation of linear stochastic partialdifferential equat...
In this paper, we obtain precise rates of convergence in the strong invariance principle for station...
AbstractResults on the convergence with probability one of stochastic approximation algorithms of th...
AbstractLet (Xn)nϵN be a sequence of real, independent, not necessarily identically distributed rand...
In this thesis, the convergence analysis of a class of weak approximations of solutions of stochasti...
Compound stochastic processes are constructed by taking the superpositive of independent copies of s...
AbstractIn this paper, we obtain precise rates of convergence in the strong invariance principle for...
The paper studies stochastic optimization (programming) problems with compound functions containing ...
Let (Xn)n[epsilon] be a sequence of real, independent, not necessarily identically distributed rando...
We generalize the results of Komlós, Major and Tusnády concerning the strong approximation of parti...
We consider a random map x → F (ω, x) and a random variable Θ(ω), and we denote by F^N (ω, x) and Θ^...
We show that if a sequence of piecewise affine linear processes converges in the strong sense with a...
International audienceWe study the convergence rates of strong approximations of stochastic processe...
his talk is devoted to strong approximations in the dependent setting. The famous results of Koml\'o...
AbstractIn this note we consider the almost sure convergence (as ϵ→0) of solution Xϵ(·), defined ove...
This thesis is concerned with numerical approximation of linear stochastic partialdifferential equat...
In this paper, we obtain precise rates of convergence in the strong invariance principle for station...
AbstractResults on the convergence with probability one of stochastic approximation algorithms of th...
AbstractLet (Xn)nϵN be a sequence of real, independent, not necessarily identically distributed rand...
In this thesis, the convergence analysis of a class of weak approximations of solutions of stochasti...
Compound stochastic processes are constructed by taking the superpositive of independent copies of s...
AbstractIn this paper, we obtain precise rates of convergence in the strong invariance principle for...
The paper studies stochastic optimization (programming) problems with compound functions containing ...
Let (Xn)n[epsilon] be a sequence of real, independent, not necessarily identically distributed rando...
We generalize the results of Komlós, Major and Tusnády concerning the strong approximation of parti...