Generalized Gaussian Error Calculus
Michael Grabe, "Generalized Gaussian Error Calculus"
Springer | 2010 | ISBN: 3642033040 | 284 pages | PDF | 3,5 MB
The proposed book Generalized Gaussian Error Calculus addresses for the first time since 200 years 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. What's more, random errors are asked to conform to the idea of what the author calls well-defined measuring conditions.
This 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 exed by an appropriate worst case estimation.
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Michael Grabe, "Generalized Gaussian Error Calculus"
Springer | 2010 | ISBN: 3642033040 | 284 pages | PDF | 3,5 MB
The proposed book Generalized Gaussian Error Calculus addresses for the first time since 200 years 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. What's more, random errors are asked to conform to the idea of what the author calls well-defined measuring conditions.
This 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 exed by an appropriate worst case estimation.
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