LEADER 03662nam 22006852 450 001 9910965967903321 005 20151005020623.0 010 $a1-107-23583-9 010 $a1-139-61618-8 010 $a1-139-62548-9 010 $a1-139-61060-0 010 $a1-139-60893-2 010 $a1-139-13508-2 010 $a1-139-61246-8 010 $a1-299-25771-2 035 $a(CKB)2560000000098642 035 $a(EBL)1099890 035 $a(OCoLC)827947115 035 $a(SSID)ssj0000833623 035 $a(PQKBManifestationID)11449185 035 $a(PQKBTitleCode)TC0000833623 035 $a(PQKBWorkID)10936473 035 $a(PQKB)10304236 035 $a(UkCbUP)CR9781139135085 035 $a(MiAaPQ)EBC1099890 035 $a(Au-PeEL)EBL1099890 035 $a(CaPaEBR)ebr10659313 035 $a(CaONFJC)MIL457021 035 $a(PPN)261312898 035 $a(EXLCZ)992560000000098642 100 $a20110729d2013|||| uy| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMeasurement uncertainty and probability /$fRobin Willink 205 $a1st ed. 210 1$aCambridge :$cCambridge University Press,$d2013. 215 $a1 online resource (xvii, 276 pages) $cdigital, PDF file(s) 300 $aTitle from publisher's bibliographic system (viewed on 05 Oct 2015). 311 08$a1-107-02193-6 311 08$a1-139-62176-9 320 $aIncludes bibliographical references and index. 327 $aFoundational ideas in measurement -- Components of error or uncertainty -- Foundational ideas in probability and statistics -- The randomization of systematic errors -- Beyond the standard confidence interval -- Final preparation -- Evaluation using the linear approximation -- Evaluation without the linear approximation -- Uncertainty information fit for purpose -- Measurement of vectors and functions -- Why take part in a measurement comparison? -- Other philosophies -- An assessment of objective Bayesian methods -- Guide to the expression of uncertainty in measurement -- Measurement near a limit, an insoluble problem? 330 $aA 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. 517 3 $aMeasurement Uncertainty & Probability 606 $aMeasurement uncertainty (Statistics) 606 $aProbabilities 615 0$aMeasurement uncertainty (Statistics) 615 0$aProbabilities. 676 $a519.2 686 $aSCI055000$2bisacsh 700 $aWillink$b Robin$f1961-$01844176 801 0$bUkCbUP 801 1$bUkCbUP 906 $aBOOK 912 $a9910965967903321 996 $aMeasurement uncertainty and probability$94426433 997 $aUNINA