02711cam a22002655a 45000010009000000050017000090080041000260200018000670400033000850820019001181000027001372450062001642600052002263000035002785040051003135050812003645200986011766500048021626500026022106510010022366530031022469420008022779990017022859520143023021736190720180228113629.0120626s2013 enka frb 001 0 eng d a9781107021938 aDLCbengcDLCdDLCdEG-ScBUE04222a519.2bWIL1 aWillink, Robin,d1961-10aMeasurement uncertainty and probability /cRobin Willink. aCambridge :bCambridge University Press,c2013. axvii, 276 p. :bill. ;c26 cm. aIncludes bibliographical references and index.8 aMachine generated contents note: Part I. Principles: 1. Introduction; 2. Foundational ideas in measurement; 3. Components of error or uncertainty; 4. Foundational ideas in probability and statistics; 5. The randomization of systematic errors; 6. Beyond the standard confidence interval; Part II. Evaluation of Uncertainty: 7. Final preparation; 8. Evaluation using the linear approximation; 9. Evaluation without the linear approximations; 10. Uncertainty information fit for purpose; Part III. Related Topics: 11. Measurement of vectors and functions; 12. Why take part in a measurement comparison?; 13. Other philosophies; 14. An assessment of objective Bayesian methods; 15. A guide to the expression of uncertainty in measurement; 16. Measurement near a limit - an insoluble problem?; References; Index. a"A measurement result is incomplete without a statement of its 'uncertainty' or 'margin of error'. But what does this statement actually tell us? By examining the practical meaning of probability, this book discusses what is meant by a '95 percent interval of measurement uncertainty', and how such an interval can be calculated. The book argues that the concept of an unknown 'target value' is essential if probability is to be used as a tool for evaluating measurement uncertainty. It uses statistical concepts, such as a conditional confidence interval, to present 'extended' classical methods for evaluating measurement uncertainty. The use of the Monte Carlo principle for the simulation of experiments is described. Useful for researchers and graduate students, the book also discusses other philosophies relating to the evaluation of measurement uncertainty. It employs clear notation and language to avoid the confusion that exists in this controversial field of science"-- 7aMeasurement uncertainty (Statistics)2BUEsh 7aProbabilities.2BUEsh 2BUEsh bGGENbENGELCcFebruary2018 2ddc c26204d26176 00102ddc40708BaccahaMAINbMAINc1STd2018-02-28ePurchaseg607.00h21201l0o519.2 WILp000047308r2022-08-24v759.00w2018-02-28yBB