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Salicone S. — Measurement Uncertainty: An Approach Via the Mathematical Theory of Evidence
Salicone S. — Measurement Uncertainty: An Approach Via the Mathematical Theory of Evidence

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Название: Measurement Uncertainty: An Approach Via the Mathematical Theory of Evidence

Автор: Salicone S.


The expression of uncertainty in measurement is a challenging aspect for researchers and engineers working in instrumentation and measurement because it involves physical, mathematical and philosophical issues. This problem is intensified by the limitations of the probabilistic approach used by the current standard (GUM). This text is the first to make full use of the mathematical theory of evidence to express the uncertainty in measurements. It gives an overview of the current standard, then pinpoints and constructively resolves its limitations through its unique approach. The text presents various tools for evaluating uncertainty, beginning with the probabilistic approach and concluding with the expression of uncertainty using random-fuzzy variables. The exposition is driven by numerous examples. The book is designed for immediate use and application in research and laboratory work. Prerequisites for students include courses in statistics and measurement science. Apart from a classroom setting, this book can be used by practitioners in a variety of fields (including applied mathematics, applied probability, electrical and computer engineering, and experimental physics), and by such institutions as the IEEE, ISA, and National Institute of Standards and Technology.

Язык: en

Рубрика: Математика/

Статус предметного указателя: Готов указатель с номерами страниц

ed2k: ed2k stats

Год издания: 2006

Количество страниц: 228

Добавлена в каталог: 13.05.2008

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Предметный указатель
$\alpha$-cut      18
Aggregate      100
Aggregation operation      99
Archimedean t-conorm      108
Archimedean t-norm      104
Averaging operator      101
Basic probability assignment      37
Bayesian belief function      34
Bayesian theory      34
Bayes’ rule of additivity      34
Belief function      32
Body of evidence      44
Central limit theorem      8
Confidence interval      7
Correlation      6
Correlation coefficient      6
Credibility factor      195
Decision making      14
Degree of belief      38
Degree of doubt      44
Error      2
Focal element      44
Frame of discernment      37
Fuzzy intersection      101
Fuzzy operator      99
Fuzzy union      106
Fuzzy variable      13
Fuzzy variable of type 2      77
Idempotency      103
Incomplete knowledge      15 73
Indirect measurement      99
Interval analysis      78
Interval of confidence of type 1      78
Interval of confidence of type 2      77
Kerre method      201
Level of confidence      7
Membership function      18
Membership grade      100
Monte Carlo simulation      136
Nakamura method      201
Necessity function      50
Nested focal element      49
Ordering rule      194
OWA operator      113
Perfect evidence      56 67
Plausibility function      44
Possibility distribution function      54 87
Possibility function      50
Possibility theory      13
Probability density function      38
probability distribution      8 66
Probability distribution function      87
Probability function      32
Probability theory      10
Random      2
Random phenomena      74
Random variable      13
Random-fuzzy variable      77
Rule of additivity      32
Singleton      38
Standard fuzzy intersection      103
Standard fuzzy union      108
Subidempotency      103
Superidempotency      108
Systematic      2
t-conorm      101
T-norm      101
Theory of Error      2
Theory of Evidence      13
Theory of Measurement      1
Theory of Uncertainty      4
Total belief      67
Total ignorance      16
Total knowledge      73
TRUE value      2
Uncertainty      4
Vacuous belief function      35
Weighting vector      113
Yager method      198
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