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Efficient implementation of a Monte Carlo method for uncertainty evaluation in dynamic measurements.

Eichstaumldt, S*; Link, A*; Harris, P M; Elster, C* (2012) Efficient implementation of a Monte Carlo method for uncertainty evaluation in dynamic measurements. Metrologia, 49 (3). pp. 401-410.

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Abstract

Measurement of quantities having time-dependent values such as force, acceleration or pressure is a topic of growing importance in metrology. The application of the Guide to the Expression of Uncertainty in Measurement (GUM) and its Supplements to the evaluation of uncertainty for such quantities is challenging. We address the ecient implementation of the Monte Carlo method described in GUM Supplements 1 and 2 for this task. The starting point is a time-domain observation equation. The steps of deriving a corresponding measurement model, the assignment of probability distributions to the input quantities in the model, and the propagation of the distributions through the model are all considered. A direct implementation of a Monte Carlo method can be intractable on many computers since the storage requirement of the method can be large compared with the available computer memory. Two memory-effcient alternatives to the direct implementation are proposed. One approach is based on applying updating formulae for calculating means, variances and point-wise histograms. The second approach is based on evaluating the measurement model sequentially in time. A simulated example is used to compare the performance of the direct and alternative procedures.

Item Type: Article
Subjects: Mathematics and Scientific Computing
Mathematics and Scientific Computing > Measurement Uncertainties
Identification number/DOI: 10.1088/0026-1394/49/3/401
Last Modified: 02 Feb 2018 13:14
URI: http://eprintspublications.npl.co.uk/id/eprint/5492

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