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

We show that the probability that a multilinear polynomial $f$ of independentrandom variables exceeds its mean by $\lambda$ is at most $e^{-\lambda^2 / (R^qVar(f))}$ for sufficiently small $\lambda$, where $R$ is an absolute constant.This matches (up to constants in the exponent) what one would expect from thecentral limit theorem. Our methods handle a variety of types of randomvariables including Gaussian, Boolean, exponential, and Poisson. Previous workby Kim-Vu and Schudy-Sviridenko gave bounds of the same form that involved lessnatural parameters in place of the variance.

Bernstein-like Concentration and Moment Inequalities for Polynomials of Independent Random Variables: Multilinear Case