Authors:
- Defines a rigorous mathematical setting that fosters the identification of an effective uncertainty propagation method
- Offers a beneficial alternative approach using examples of uncertainty propagation
- Includes an author-designed, downloadable program that allows readers to interact with the proposed approach
Part of the book series: Springer Series in Measurement Science and Technology (SSMST)
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Table of contents (25 chapters)
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Front Matter
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The Background of Measurement Uncertainty
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Front Matter
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The Mathematical Theory of Evidence
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Front Matter
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The Fuzzy Set Theory and the Theory of Evidence
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Front Matter
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Measurement Uncertainty Within the Mathematical Framework of the Theory of Evidence
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Front Matter
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About this book
While the first part of the book introduces measurement uncertainty, the Theory of Evidence, and fuzzy sets, the following parts bring together these concepts and derive an effective methodology for the evaluation and expression of measurement uncertainty. A supplementary downloadable program allows the readers tointeract with the proposed approach by generating and combining RFVs through custom measurement functions. With numerous examples of applications, this book provides a comprehensive treatment of the RFV approach to uncertainty that is suitable for any graduate student or researcher with interests in the measurement field.
Authors and Affiliations
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Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Milano, Italy
Simona Salicone
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CERN, Geneva, Switzerland
Marco Prioli
About the authors
Marco Prioli is an IEEE Instrumentation and Measurement Society member. He is also a memeber of the Italian Association for Electrical and Electronic Measurements (GMEE).
Bibliographic Information
Book Title: Measuring Uncertainty within the Theory of Evidence
Authors: Simona Salicone, Marco Prioli
Series Title: Springer Series in Measurement Science and Technology
DOI: https://doi.org/10.1007/978-3-319-74139-0
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer International Publishing AG, part of Springer Nature 2018
Hardcover ISBN: 978-3-319-74137-6Published: 07 May 2018
Softcover ISBN: 978-3-030-08924-5Published: 24 January 2019
eBook ISBN: 978-3-319-74139-0Published: 23 April 2018
Series ISSN: 2198-7807
Series E-ISSN: 2198-7815
Edition Number: 1
Number of Pages: XV, 330
Number of Illustrations: 13 b/w illustrations, 141 illustrations in colour