Refined Convergence Rates of the Good-Turing Estimator

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Good-Turing (GT) estimator is perhaps the most popular framework for modelling large alphabet distributions. Classical results show that the GT estimator convergences to the occupancy probability, formally defined as the total probability of words that appear exactly k times in the sample. In this work we introduce new convergence guarantees for the GT estimator, based on worst-case MSE analysis. Our results refine and improve upon currently known bounds. Importantly, we introduce a simultaneous convergence rate to the entire collection of occupancy probabilities.

Original languageEnglish
Title of host publication2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665403122
DOIs
StatePublished - 2021
Event2021 IEEE Information Theory Workshop, ITW 2021 - Virtual, Online, Japan
Duration: 17 Oct 202121 Oct 2021

Publication series

Name2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings

Conference

Conference2021 IEEE Information Theory Workshop, ITW 2021
Country/TerritoryJapan
CityVirtual, Online
Period17/10/2121/10/21

All Science Journal Classification (ASJC) codes

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Information Systems
  • Software

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