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

Calculating the Shannon entropy for symbolic sequences has been widelyconsidered in many fields. For descriptive statistical problems such asestimating the N-gram entropy of English language text, a common approach is touse as much data as possible to obtain progressively more accurate estimates.However in some instances, only short sequences may be available. This givesrise to the question of how many samples are needed to compute entropy. In thispaper, we examine this problem and propose a method for estimating the numberof samples required to compute Shannon entropy for a set of ranked symbolicnatural events. The result is developed using a modified Zipf-Mandelbrot lawand the Dvoretzky-Kiefer-Wolfowitz inequality, and we propose an algorithmwhich yields an estimate for the minimum number of samples required to obtainan estimate of entropy with a given confidence level and degree of accuracy.

Determining the Number of Samples Required to Estimate Entropy in Natural Sequences