English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

We obtain non-asymptotic Gaussian concentration bounds for the differencebetween the invariant measure $\nu$ of an ergodic Brownian diffusion processand the empirical distribution of an approximating scheme with decreasing timestep along a suitable class of (smooth enough) test functions f such that f --$\nu$(f) is a coboundary of the infinitesimal generator. We show that thesebounds can still be improved when the (squared) Fr{\"o}benius norm of thediffusion coefficient lies in this class. We apply these bounds to designcomputable non-asymptotic confidence intervals for the approximating scheme. Asa theoretical application, we finally derive non-asymptotic deviation boundsfor the almost sure Central Limit Theorem.

Non-Asymptotic Gaussian Estimates for the Recursive Approximation of the Invariant Measure of a Diffusion