Generalized Gaussian Error Calculus

ISBN-13: 9783642424366 / Angielski / Miękka / 2014 / 301 str.

Michael Grabe

Opis

The book of nature is written in the language of mathematics Galileo Galilei, 1623 Metrology strives to supervise the ?ow of the measurand s true values throughconsecutive, arbitrarilyinterlockingseriesofmeasurements.Tohi- light this feature the term traceability has been coined. Traceability is said to be achieved, given the true values of each of the physical quantities entering and leaving the measurement are localized by speci?ed measu- ment uncertainties. The classical Gaussian error calculus is known to be con?ned to the tre- ment of random errors. Hence, there is no distinction between the true value of a measurand on the one side and the expectation of the respective es- mator on the other. This became apparent not until metrologists considered the e?ect of so-called unknown systematic errors. Unknown systematic errors are time-constant quantities unknown with respect to magnitude and sign. While random errors are treated via distribution densities, unknown syst- atic errors can only be assessed via intervals of estimated lengths. Unknown systematic errors were, in fact, addressed and discussed by Gauss himself. Gauss, however, argued that it were up to the experimenter to eliminate their causes and free the measured values from their in?uence."

Recenzje

From the reviews:

"This book is aimed at the metrology community. ... The approach elaborated in this book assesses unknown systematic errors via intervals of estimated lengths. ... the author proposes the generalized Gaussian approach presented here as one which produces reliable measurement uncertainties meeting the demands of traceability." (Rainer Schlittgen, Zentralblatt MATH, Vol. 1210, 2011)

Spis treści

Basics of Metrology.- True Values and Traceability.- Models and Approaches.- Generalized Gaussian Error Calculus.- The New Uncertainties.- Treatment of Random Errors.- Treatment of Systematic Errors.- Error Propagation.- Means and Means of Means.- Functions of Erroneous Variables.- Method of Least Squares.- Essence of Metrology.- Dissemination of Units.- Multiples and Sub-multiples.- Founding Pillars.- Fitting of Straight Lines.- Preliminaries.- Straight Lines: Case (i).- Straight Lines: Case (ii).- Straight Lines: Case (iii).- Fitting of Planes.- Preliminaries.- Planes: Case (i).- Planes: Case (ii).- Planes: Case (iii).- Fitting of Parabolas.- Preliminaries.- Parabolas: Case (i).- Parabolas: Case (ii).- Parabolas: Case (iii).- Non-Linear Fitting.- Series Truncation.- Transformation.

O autorze

1967 Graduation in Physics at the Technical University of Stuttgart

1970 Doctorate at the Technical University of Braunschweig

1970 – 1975 Scientific assistant and lecturer at the Technical University of Braunschweig

1975 – 2004 Member of Staff at the Physikalische Technischer Bundesanstalt Braunschweig, commissioned to legal metrology, computerized interferometric measurment of length, measurement uncertainties and the adjustment of physical constants

Blurb

For the first time in 200 years Generalized Gaussian Error Calculus addresses a rigorous, complete and self-consistent revision of the Gaussian error calculus. Since experimentalists realized that measurements in general are burdened by unknown systematic errors, the classical, widespread used evaluation procedures scrutinizing the consequences of random errors alone turned out to be obsolete. As a matter of course, the error calculus to-be, treating random and unknown systematic errors side by side, should ensure the consistency and traceability of physical units, physical constants and physical quantities at large.

The generalized Gaussian error calculus considers unknown systematic errors to spawn biased estimators. Beyond, random errors are asked to conform to the idea of what the author calls well-defined measuring conditions.

The approach features the properties of a building kit: any overall uncertainty turns out to be the sum of a contribution due to random errors, to be taken from a confidence interval as put down by Student, and a contribution due to unknown systematic errors, as expressed by an appropriate worst case estimation.