Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

Uncertainty (Information theory) --- Dempster-Shafer theory. --- Random variables. --- Chance variables --- Stochastic variables --- Probabilities --- Variables (Mathematics) --- Belief functions (Probabilities) --- Dempster-Shafer theory of evidence --- Shafer theory, Dempster --- -Probabilities --- Measure of uncertainty (Information theory) --- Shannon's measure of uncertainty --- System uncertainty --- Information measurement --- Questions and answers --- Mathematics. --- Electronics. --- Distribution (Probability theory. --- Measure and Integration. --- Electronics and Microelectronics, Instrumentation. --- Measurement Science and Instrumentation. --- Probability Theory and Stochastic Processes. --- Math --- Science --- Electrical engineering --- Physical sciences --- Distribution functions --- Frequency distribution --- Characteristic functions --- Measure theory. --- Microelectronics. --- Physical measurements. --- Measurement   . --- Probabilities. --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk --- Measuring --- Mensuration --- Technology --- Metrology --- Physical measurements --- Measurements, Physical --- Mathematical physics --- Measurement --- Microminiature electronic equipment --- Microminiaturization (Electronics) --- Electronics --- Microtechnology --- Semiconductors --- Miniature electronic equipment --- Lebesgue measure --- Measurable sets --- Measure of a set --- Algebraic topology --- Integrals, Generalized --- Measure algebras --- Rings (Algebra) --- Measurement.

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

Operational research. Game theory --- Measuring methods in physics --- Mathematical physics --- Electronics --- Chemical technology --- meetmethoden --- differentiaalvergelijkingen --- stochastische analyse --- elektronica --- kansrekening

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

This reprint focuses on a very important topic in metrology, which is represent by measurement uncertainty. Any good metrologist or scientist in engineering knows that no measurement makes sense without an associated uncertainty value: without an uncertainty value, no decision can be taken; no comparisons can be made; no conformity can be assessed. Any decision, comparison or conformity assessment made without considering the measurement uncertainty affecting the measurement value is completely useless and meaningless. Stated that, it becomes very clear that uncertainty in measurement plays indeed a very important rule in our everyday life. This is the reason why there is a great need to have a fruitful academic and scientific discussion on this topic. We have been speaking about measurement uncertainty for less than 30 years, since the concept of "measurement uncertainty" has been introduced in 1995 by the "Guide to the expression of uncertainty in measurement" (GUM). Thirty years seems to be many, but still the concept of measurement uncertainty has not been spread worldwide and the GUM is a document that is not known everywhere. On the other hand, this document should be considered not only in academic scenario, but also in any technical and industrial scenario, where it is pivotal to know the meaning of measurement uncertainty, identify the uncertainty contributions and know how these contributions affect the final measurement result.

Measurement uncertainty (Statistics) --- Measurement

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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. .

Measurement uncertainty (Statistics) --- Uncertainty (Information theory) --- Mathematics. --- Probabilities. --- Probability Theory and Stochastic Processes. --- Measure of uncertainty (Information theory) --- Shannon's measure of uncertainty --- System uncertainty --- Information measurement --- Probabilities --- Questions and answers --- Error, Margin of (Statitics) --- Margin of error (Statistics) --- Instrumental variables (Statistics) --- Measurement --- Uncertainty --- Distribution (Probability theory. --- Distribution functions --- Frequency distribution --- Characteristic functions --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Mathematical statistics --- Risk

Choose an application

• Reference Manager
• EndNote
• RefWorks (Direct export to RefWorks)

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.

Operational research. Game theory --- Measuring methods in physics --- Mathematical physics --- Electronics --- Chemical technology --- meetmethoden --- differentiaalvergelijkingen --- stochastische analyse --- elektronica --- kansrekening