Normalization-Invariant Fuzzy Logic Operations Explain Empirical Success of Student Distributions in Describing Measurement Uncertainty
- verfasst von
- Hamza Alkhatib, Boris Kargoll, Ingo Neumann, Vladik Kreinovich
- Abstract
In engineering practice, usually measurement errors are described by normal distributions. However, in some cases, the distribution is heavy-tailed and thus, not normal. In such situations, empirical evidence shows that the Student distributions are most adequate. The corresponding recommendation – based on empirical evidence – is included in the International Organization for Standardization guide. In this paper, we explain this empirical fact by showing that a natural fuzzy-logic-based formalization of commonsense requirements leads exactly to the Student’s distributions.
- Organisationseinheit(en)
-
Geodätisches Institut
- Externe Organisation(en)
-
University of Texas at El Paso
- Typ
- Beitrag in Buch/Sammelwerk
- Band
- 648
- Seiten
- 300-306
- Anzahl der Seiten
- 7
- Publikationsdatum
- 2018
- Publikationsstatus
- Veröffentlicht
- Peer-reviewed
- Ja
- ASJC Scopus Sachgebiete
- Steuerungs- und Systemtechnik, Informatik (insg.)
- Elektronische Version(en)
-
https://doi.org/10.1007/978-3-319-67137-6_34 (Zugang:
Offen)
