Measurement Uncertainty: An Approach via the Mathematical Theory of Evidence - Springer Series in Reliability Engineering (Paperback)Simona Salicone (author)
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The expression of uncertainty in measurement poses a challenge since it involves physical, mathematical, and philosophical issues. This problem is intensified by the limitations of the probabilistic approach used by the current standard (the GUM Instrumentation Standard). This text presents an alternative approach. It makes full use of the mathematical theory of evidence to express the uncertainty in measurements. Coverage provides an overview of the current standard, then pinpoints and constructively resolves its limitations. Numerous examples throughout help explain the book's unique approach.
Publisher: Springer-Verlag New York Inc.
Number of pages: 228
Weight: 373 g
Dimensions: 235 x 155 x 12 mm
Edition: Softcover reprint of hardcover 1st ed. 2007
From the reviews:
"This book is the first to make full use of the mathematical theory of evidence to express the uncertainty in measurement. ... This book can be useful for researchers (and practitioners) in the fields of statistics and measurement theory." (Robert Fuller, Mathematical Reviews, Issue 2007 j)
"This mathematics book is the first one to propose a different way of representing measurement uncertainty using fuzzy variables ... . It is rare that a book of mathematics is so easy to read as this one, even for people unfamiliar with the topic of fuzzy variables. ... The book is organised for and addressed to students ... . It is also meant to be a ready-to-use tool for practitioners in measurements. ... will interest researchers and specialists in the science of measurements." (Mariana Buzduga, International Journal of Acoustics and Vibration, Vol. 13 (1), 2008)
"The book under review is the first to make full use of this theory to express the uncertainly in measurements. ... The book is designed for immediate use and applications in research and laboratory work in various fields, including applied probability, electrical and computer engineering, and experimental physics. Prerequisites for students include courses in statistics and measurement science." (Oleksandr Kukush, Zentralblatt MATH, Vol. 1144, 2008)
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