From Quantifying and Propagating Uncertainty to Quantifying and Propagating Both Uncertainty and Reliability

Practice-Motivated Approach to Measurement Planning and Data Processing

verfasst von: Niklas R. Winnewisser, Michael Beer, Vladik Kreinovich, Olga Kosheleva
Abstract: When we process data, it is important to take into account that data comes with uncertainty. There exist techniques for quantifying uncertainty and propagating this uncertainty through the data processing algorithms. However, most of these techniques do not take into account that in th real world, measuring instruments are not 100% reliable – they sometimes malfunction and produce values which are far off from the measured values of the corresponding quantities. How can we take into account both uncertainty and reliability? In this paper, we consider several possible scenarios, and we show, for each scenario, what is the natural way to plan the measurements and to quantify and propagate the resulting uncertainty and reliability.
Organisationseinheit(en): Gottfried Wilhelm Leibniz Universität Hannover
Externe Organisation(en): University of Texas at El Paso
Typ: Aufsatz in Konferenzband
Seiten: 389-402
Anzahl der Seiten: 14
Publikationsdatum: 05.01.2025
Publikationsstatus: Veröffentlicht
Peer-reviewed: Ja
ASJC Scopus Sachgebiete: Steuerungs- und Systemtechnik, Signalverarbeitung, Computernetzwerke und -kommunikation
Elektronische Version(en): https://doi.org/10.1007/978-3-031-74003-9_31 (Zugang: Geschlossen)

BibTeX

@inproceedings{35a78f2240de4f2eace9e9cb85b98355,
title = "From Quantifying and Propagating Uncertainty to Quantifying and Propagating Both Uncertainty and Reliability: Practice-Motivated Approach to Measurement Planning and Data Processing",
abstract = "When we process data, it is important to take into account that data comes with uncertainty. There exist techniques for quantifying uncertainty and propagating this uncertainty through the data processing algorithms. However, most of these techniques do not take into account that in th real world, measuring instruments are not 100% reliable – they sometimes malfunction and produce values which are far off from the measured values of the corresponding quantities. How can we take into account both uncertainty and reliability? In this paper, we consider several possible scenarios, and we show, for each scenario, what is the natural way to plan the measurements and to quantify and propagate the resulting uncertainty and reliability.",
keywords = "Data processing, Measurement reliability, Measurement uncertainty",
author = "Winnewisser, {Niklas R.} and Michael Beer and Vladik Kreinovich and Olga Kosheleva",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.; 20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2024 ; Conference date: 22-07-2024 Through 26-07-2024",
year = "2025",
month = jan,
day = "5",
doi = "10.1007/978-3-031-74003-9_31",
language = "English",
isbn = "9783031740022",
series = "Lecture Notes in Networks and Systems",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "389--402",
editor = "Marie-Jeanne Lesot and Susana Vieira and Reformat, {Marek Z.} and Carvalho, {Jo{\~a}o Paulo} and Fernando Batista and Bernadette Bouchon-Meunier and Yager, {Ronald R.}",
booktitle = "Information Processing and Management of Uncertainty in Knowledge-Based Systems - 20th International Conference, IPMU 2024, Proceedings",
address = "Germany",
}