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Measurement uncertainty

Working Group 8.42
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Comparison of measurement results, reliable decision-making and conformity assessment require the evaluation of uncertainties associated with measurement results. The ability to compare measurements made in different places and at different times underpins international metrology. The Guide to the Expression of Uncertainty in Measurement (Opens external link in new windowGUM) provides guidance for the evaluation of uncertainties, and it has been applied successfully in many applications throughout metrology.


Illustration of Monte Carlo method according to Opens external link in new windowSupplement 1 to the GUM.

In recent years metrology has expanded to support new fields to address societal challenges relating to energy and sustainability, climate and environmental monitoring, life sciences and health, using measurement modalities such as imaging, spectroscopy, earth observation and sensor networks. Reliable uncertainty evaluation is particularly important in these applications, e.g. to safeguard the diagnosis of a tumor in quantitative imaging or to reliably monitor air pollution. The GUM does not adequately address the challenges arising in these applications, and the development of statistical procedures for improved uncertainty evaluation is an urgent need.

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The focus of PTB’s Working Group 8.42 is on the development of Bayesian methods for the evaluation of uncertainties. The development is carried out within the context of different research areas of data analysis such as large-scale data analysis or deep learning. Bayesian inference procedures suitable for the extension of the current GUM methodology are also part of the current research in PTB’s Working Group 8.42. Examples include simple means to assign distributions representing the available prior knowledge, or procedures for the numerical calculation of results. Open source software support is provided to ease the application of the research results.

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Publication single view


Title: Efficient implementation of a Monte Carlo method for uncertainty evaluation in dynamic measurements
Author(s): S. Eichstädt, A. Link, P. M. Harris and C. Elster
Journal: Metrologia
Year: 2012
Volume: 49
Issue: 3
Pages: 401
DOI: 10.1088/0026-1394/49/3/401
Tags: 8.42, Dynamik, Unsicherheit
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 efficient 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-efficient 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.

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