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Measuring uncertainty within the theory of evidence

This monograph considers the evaluation and expression of measurement uncertainty within the mathematical framework of the Theory of Evidence. With a new perspective on the metrology science, the text paves the way for innovative applications in a wide range of areas. Building on Simona Salicone’s M...

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Detalles Bibliográficos
Autores principales: Salicone, Simona, Prioli, Marco
Lenguaje:eng
Publicado: Springer 2018
Materias:
Acceso en línea:https://dx.doi.org/10.1007/978-3-319-74139-0
http://cds.cern.ch/record/2316198
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author Salicone, Simona
Prioli, Marco
author_facet Salicone, Simona
Prioli, Marco
author_sort Salicone, Simona
collection CERN
description This monograph considers the evaluation and expression of measurement uncertainty within the mathematical framework of the Theory of Evidence. With a new perspective on the metrology science, the text paves the way for innovative applications in a wide range of areas. Building on Simona Salicone’s Measurement Uncertainty: An Approach via the Mathematical Theory of Evidence, the material covers further developments of the Random Fuzzy Variable (RFV) approach to uncertainty and provides a more robust mathematical and metrological background to the combination of measurement results that leads to a more effective RFV combination method. 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 to interact 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. .
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spelling cern-23161982021-07-30T13:11:20Zdoi:10.1007/978-3-319-74139-0http://cds.cern.ch/record/2316198engSalicone, SimonaPrioli, MarcoMeasuring uncertainty within the theory of evidenceMathematical Physics and MathematicsThis monograph considers the evaluation and expression of measurement uncertainty within the mathematical framework of the Theory of Evidence. With a new perspective on the metrology science, the text paves the way for innovative applications in a wide range of areas. Building on Simona Salicone’s Measurement Uncertainty: An Approach via the Mathematical Theory of Evidence, the material covers further developments of the Random Fuzzy Variable (RFV) approach to uncertainty and provides a more robust mathematical and metrological background to the combination of measurement results that leads to a more effective RFV combination method. 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 to interact 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. .Springeroai:cds.cern.ch:23161982018
spellingShingle Mathematical Physics and Mathematics
Salicone, Simona
Prioli, Marco
Measuring uncertainty within the theory of evidence
title Measuring uncertainty within the theory of evidence
title_full Measuring uncertainty within the theory of evidence
title_fullStr Measuring uncertainty within the theory of evidence
title_full_unstemmed Measuring uncertainty within the theory of evidence
title_short Measuring uncertainty within the theory of evidence
title_sort measuring uncertainty within the theory of evidence
topic Mathematical Physics and Mathematics
url https://dx.doi.org/10.1007/978-3-319-74139-0
http://cds.cern.ch/record/2316198
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