Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Information Processing and Management of Uncertainty in Knowledge-Based Systems - 20th International Conference, IPMU 2024, Proceedings |
| Herausgeber/-innen | Marie-Jeanne Lesot, Susana Vieira, Marek Z. Reformat, João Paulo Carvalho, Fernando Batista, Bernadette Bouchon-Meunier, Ronald R. Yager |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 389-402 |
| Seitenumfang | 14 |
| ISBN (elektronisch) | 978-3-031-74003-9 |
| ISBN (Print) | 9783031740022 |
| Publikationsstatus | Veröffentlicht - 5 Jan. 2025 |
| Veranstaltung | 20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2024 - Lisbon, Portugal Dauer: 22 Juli 2024 → 26 Juli 2024 |
Publikationsreihe
| Name | Lecture Notes in Networks and Systems |
|---|---|
| Band | 1174 LNNS |
| ISSN (Print) | 2367-3370 |
| ISSN (elektronisch) | 2367-3389 |
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.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Informatik (insg.)
- Signalverarbeitung
- Informatik (insg.)
- Computernetzwerke und -kommunikation
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- BibTex
- RIS
Information Processing and Management of Uncertainty in Knowledge-Based Systems - 20th International Conference, IPMU 2024, Proceedings. Hrsg. / Marie-Jeanne Lesot; Susana Vieira; Marek Z. Reformat; João Paulo Carvalho; Fernando Batista; Bernadette Bouchon-Meunier; Ronald R. Yager. Springer Science and Business Media Deutschland GmbH, 2025. S. 389-402 (Lecture Notes in Networks and Systems; Band 1174 LNNS).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - From Quantifying and Propagating Uncertainty to Quantifying and Propagating Both Uncertainty and Reliability
T2 - 20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2024
AU - Winnewisser, Niklas R.
AU - Beer, Michael
AU - Kreinovich, Vladik
AU - Kosheleva, Olga
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2025/1/5
Y1 - 2025/1/5
N2 - 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.
AB - 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.
KW - Data processing
KW - Measurement reliability
KW - Measurement uncertainty
UR - http://www.scopus.com/inward/record.url?scp=85216016031&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-74003-9_31
DO - 10.1007/978-3-031-74003-9_31
M3 - Conference contribution
AN - SCOPUS:85216016031
SN - 9783031740022
T3 - Lecture Notes in Networks and Systems
SP - 389
EP - 402
BT - Information Processing and Management of Uncertainty in Knowledge-Based Systems - 20th International Conference, IPMU 2024, Proceedings
A2 - Lesot, Marie-Jeanne
A2 - Vieira, Susana
A2 - Reformat, Marek Z.
A2 - Carvalho, João Paulo
A2 - Batista, Fernando
A2 - Bouchon-Meunier, Bernadette
A2 - Yager, Ronald R.
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 July 2024 through 26 July 2024
ER -