An informed type A evaluation of standard uncertainty is here derived based on Bayesian analysis. The result is mathematically simple, easily interpretable, applicable both in the theoretical framework of the Guide to the Expression of Uncertainty in Measurement (propagation of standard uncertainties) and in that of the Supplement 1 of the Guide (propagation of distributions), valid for any size greater than or equal to 1 of the sample of present observations. The evaluation consistently addresses prior information in the form of the sample variance of a series of recorded experimental observations and in the form of an educated guess based on expert’s experience. It turns out that distinction between type A and type B evaluation is, in this context, contrived.

An informed type A evaluation of standard uncertainty valid for any sample size greater than or equal to 1 / Carobbi. - In: ACTA IMEKO. - ISSN 2221-870X. - ELETTRONICO. - 11:(2022), pp. 1-5. [10.21014/acta_imeko.v11i2.1170]

An informed type A evaluation of standard uncertainty valid for any sample size greater than or equal to 1

Carobbi
Investigation
2022

Abstract

An informed type A evaluation of standard uncertainty is here derived based on Bayesian analysis. The result is mathematically simple, easily interpretable, applicable both in the theoretical framework of the Guide to the Expression of Uncertainty in Measurement (propagation of standard uncertainties) and in that of the Supplement 1 of the Guide (propagation of distributions), valid for any size greater than or equal to 1 of the sample of present observations. The evaluation consistently addresses prior information in the form of the sample variance of a series of recorded experimental observations and in the form of an educated guess based on expert’s experience. It turns out that distinction between type A and type B evaluation is, in this context, contrived.
2022
11
1
5
Carobbi
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1267994
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