@inproceedings{0804dd40a862415585e34b417ec70c25,
title = "Refined Convergence Rates of the Good-Turing Estimator",
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.",
author = "Amichai Painsky",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE Information Theory Workshop, ITW 2021 ; Conference date: 17-10-2021 Through 21-10-2021",
year = "2021",
doi = "https://doi.org/10.1109/ITW48936.2021.9611389",
language = "الإنجليزيّة",
series = "2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE Information Theory Workshop, ITW 2021 - Proceedings",
address = "الولايات المتّحدة",
}