Approaches to evaluating measurement uncertainty
The Guide to the expression of measurement uncertainty, (GUM, JCGM 100) and its Supplement 1: propagation of distributions by a Monte Carlo method, (GUMS1, JCGM 101) are two of the most widely used documents concerning measurement uncertainty evaluation in metrology....
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2012-01-01
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| Series: | International Journal of Metrology and Quality Engineering |
| Subjects: | |
| Online Access: | https://www.metrology-journal.org/articles/ijmqe/pdf/2012/02/ijmqe120017.pdf |
| Summary: | The Guide to the expression of measurement uncertainty, (GUM, JCGM 100)
and its Supplement 1: propagation of distributions by a Monte Carlo
method, (GUMS1, JCGM 101) are two of the most widely used documents concerning
measurement uncertainty evaluation in metrology. Both documents describe three phases (a)
the construction of a measurement model, (b) the assignment of probability distributions
to quantities, and (c) a computational phase that specifies the distribution for the
quantity of interest, the measurand. The two approaches described in these two documents
agree in the first two phases but employ different computational approaches, with the GUM
using linearisations to simplify the calculations. Recent years have seen an increasing
interest in using Bayesian approaches to evaluating measurement uncertainty. The Bayesian
approach in general differs in the assignment of the probability distributions and its
computational phase usually requires Markov chain Monte Carlo (MCMC) approaches. In this
paper, we summarise the three approaches to evaluating measurement uncertainty and show
how we can regard the GUM and GUMS1 as providing approximate solutions to the Bayesian
approach. These approximations can be used to design effective MCMC algorithms. |
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| ISSN: | 2107-6839 2107-6847 |