@inproceedings{5a77188e43c24ae6aefb3532fd59e21f,
title = "Weighted entropy and its use in computer science and beyond",
abstract = "The concept of weighted entropy takes into account values of different outcomes, i.e., makes entropy context-dependent, through the weight function. We analyse analogs of the Fisher information inequality and entropy-power inequality for the weighted entropy and discuss connections with weighted Lieb{\textquoteright}s splitting inequality. The concepts of rates of the weighted entropy and information are also discussed.",
author = "Mark Kelbert and Izabella Stuhl and Yuri Suhov",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-71504-9_25",
language = "English (US)",
isbn = "9783319715032",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "293--308",
editor = "Rykov, {Vladimir V.} and Singpurwalla, {Nozer D.} and Zubkov, {Andrey M.}",
booktitle = "Analytical and Computational Methods in Probability Theory - 1st International Conference, ACMPT 2017, Proceedings",
address = "Germany",
note = "1st International Conference Analytical and Computational Methods in Probability Theory, ACMPT 2017 ; Conference date: 23-10-2017 Through 27-10-2017",
}