Add to ORKGAbstract. Let µN be the empirical measure associated to a N-sample of a given probability distribution µ on Rd. We are interested in the rate of convergence of µN to µ, when measured in the Wasserstein distance of order p> 0. We provide some satisfying non-asymptotic Lp-bounds and concentration inequalities, for any values of p> 0 and d ≥ 1. We extend also the non asymptotic Lp-bounds to stationary ρ-mixing sequences, Markov chains, and to some interacting particle systems
We provide some non asymptotic bounds, with explicit constants, that measure the rate of convergence...
We consider a Markov chain on $\mathbb{R}^d$ with invariant measure $\mu$. We are interested in the ...
AbstractAn upper bound is given for the mean square Wasserstein distance between the empirical measu...
We provide some non-asymptotic bounds, with explicit constants, that measure the rate of convergence...
We provide some non-asymptotic bounds, with explicit constants, that measure the rate of convergence...
We provide some non asymptotic bounds, with explicit constants, that measure the rate of convergence...
International audienceIn this work, we provide non-asymptotic bounds for the average speed of conver...
International audienceIn this work, we provide non-asymptotic bounds for the average speed of conver...
International audienceIn this work, we provide non-asymptotic bounds for the average speed of conver...
In this work, we provide non-asymptotic bounds for the average speed of convergence of the empirical...
In this work, we provide non-asymptotic bounds for the average speed of convergence of the empirical...
An upper bound is given for the mean square Wasserstein distance between the empirical measure of a ...
An upper bound is given for the mean square Wasserstein distance between the empirical measure of a ...
The Wasserstein distance between two probability measures on a metric spaceis a measure of closeness...
Let $\mu_N$ be the empirical measure associated to a $N$-sample of a given probability distribution ...
We provide some non asymptotic bounds, with explicit constants, that measure the rate of convergence...
We consider a Markov chain on $\mathbb{R}^d$ with invariant measure $\mu$. We are interested in the ...
AbstractAn upper bound is given for the mean square Wasserstein distance between the empirical measu...
We provide some non-asymptotic bounds, with explicit constants, that measure the rate of convergence...
We provide some non-asymptotic bounds, with explicit constants, that measure the rate of convergence...
We provide some non asymptotic bounds, with explicit constants, that measure the rate of convergence...
International audienceIn this work, we provide non-asymptotic bounds for the average speed of conver...
International audienceIn this work, we provide non-asymptotic bounds for the average speed of conver...
International audienceIn this work, we provide non-asymptotic bounds for the average speed of conver...
In this work, we provide non-asymptotic bounds for the average speed of convergence of the empirical...
In this work, we provide non-asymptotic bounds for the average speed of convergence of the empirical...
An upper bound is given for the mean square Wasserstein distance between the empirical measure of a ...
An upper bound is given for the mean square Wasserstein distance between the empirical measure of a ...
The Wasserstein distance between two probability measures on a metric spaceis a measure of closeness...
Let $\mu_N$ be the empirical measure associated to a $N$-sample of a given probability distribution ...
We provide some non asymptotic bounds, with explicit constants, that measure the rate of convergence...
We consider a Markov chain on $\mathbb{R}^d$ with invariant measure $\mu$. We are interested in the ...
AbstractAn upper bound is given for the mean square Wasserstein distance between the empirical measu...