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

This paper provides tight bounds on the R\'enyi entropy of a function of adiscrete random variable with a finite number of possible values, where theconsidered function is not one-to-one. To that end, a tight lower bound on theR\'enyi entropy of a discrete random variable with a finite support is derivedas a function of the size of the support, and the ratio of the maximal tominimal probability masses. This work was inspired by the recently publishedpaper by Cicalese et al., which is focused on the Shannon entropy, and itstrengthens and generalizes the results of that paper to R\'enyi entropies ofarbitrary positive orders. In view of these generalized bounds and the works byArikan and Campbell, non-asymptotic bounds are derived for guessing moments andlossless data compression of discrete memoryless sources.

Tight Bounds on the R\'enyi Entropy via Majorization with Applications to Guessing and Compression