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/

Global: Zendy Plus annual

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

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Let $Z$ be an $n$-dimensional Gaussian vector and let $f: \mathbb R^n \to\mathbb R$ be a convex function. We show that: $$\mathbb P \left( f(Z) \leq\mathbb E f(Z) -t\sqrt{ {\rm Var} f(Z)} \right) \leq \exp(-ct^2),$$ for all$t>1$, where $c>0$ is an absolute constant. As an application we derivevariance-sensitive small ball probabilities for Gaussian processes.

A Gaussian small deviation inequality for convex functions