We detail an approach to developing Stein's method for bounding integral metrics on probability measures defined on a Riemannian manifold M. Our approach exploits the relationship between the generator of a diffusion on M having having a target invariant measure and its characterising Stein operator. We consider a pair of such diffusions with different starting points, and through analysis of the distance process between the pair, derive Stein factors, which bound the solution to the Stein equation and its derivatives. The Stein factors contain curvature-dependent terms and reduce to those currently available for R m , and moreover imply that the bounds for R m remain valid when M is a flat manifold
International audienceThe Stein's method is a popular method used to derive upper-bounds of distance...
In extending Stein's method to new target distributions, the first step is to find a Stein operator ...
Lower Ricci curvature bounds play a crucial role in several deep geometric and functional inequaliti...
The overarching theme of this thesis is the study of Stein's method on manifolds. We detail an adapt...
AbstractWe construct Otto–Villani's coupling for general reversible diffusion processes on a Riemann...
We provide a general steady-state diffusion approximation result which bounds the Wasserstein distan...
AbstractGiven a random variable F regular enough in the sense of the Malliavin calculus, we are able...
We construct Otto-Villani’s coupling for general reversible diffusion processes on a Riemannian mani...
peer reviewedWe build on the formalism developed in Ernst et al. (First order covariance inequalitie...
AbstractThe topic of this paper are convexity properties of free energy functionals on the space P2(...
28 pagesIn a spirit close to classical Stein's method, we introduce a new technique to derive first ...
For a complete connected Riemannian manifold M let V∊ C^2(M) be such that µ(dx)=exp(-V(x))vol(dx) is...
Lower Ricci curvature bounds play a crucial role in several deep geometric and functional inequaliti...
Stein's method of obtaining distributional approximations is developed in the context of functional ...
Stein's method has been widely used to achieve distributional approximations for probability distrib...
International audienceThe Stein's method is a popular method used to derive upper-bounds of distance...
In extending Stein's method to new target distributions, the first step is to find a Stein operator ...
Lower Ricci curvature bounds play a crucial role in several deep geometric and functional inequaliti...
The overarching theme of this thesis is the study of Stein's method on manifolds. We detail an adapt...
AbstractWe construct Otto–Villani's coupling for general reversible diffusion processes on a Riemann...
We provide a general steady-state diffusion approximation result which bounds the Wasserstein distan...
AbstractGiven a random variable F regular enough in the sense of the Malliavin calculus, we are able...
We construct Otto-Villani’s coupling for general reversible diffusion processes on a Riemannian mani...
peer reviewedWe build on the formalism developed in Ernst et al. (First order covariance inequalitie...
AbstractThe topic of this paper are convexity properties of free energy functionals on the space P2(...
28 pagesIn a spirit close to classical Stein's method, we introduce a new technique to derive first ...
For a complete connected Riemannian manifold M let V∊ C^2(M) be such that µ(dx)=exp(-V(x))vol(dx) is...
Lower Ricci curvature bounds play a crucial role in several deep geometric and functional inequaliti...
Stein's method of obtaining distributional approximations is developed in the context of functional ...
Stein's method has been widely used to achieve distributional approximations for probability distrib...
International audienceThe Stein's method is a popular method used to derive upper-bounds of distance...
In extending Stein's method to new target distributions, the first step is to find a Stein operator ...
Lower Ricci curvature bounds play a crucial role in several deep geometric and functional inequaliti...