Overview
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 (
GUM) 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 Supplement 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.
Research
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.
Software
Publications
Publication single view
Article
| Title: | Linear Mixed Models: Gum and Beyond |
|---|---|
| Author(s): | B. Arendacká, A. Täubner, S. Eichstädt, T. Bruns and C. Elster |
| Journal: | Measurement Science Review |
| Year: | 2014 |
| Volume: | 14 |
| Issue: | 2 |
| Pages: | 52-61 |
| DOI: | 10.2478/msr-2014-0009 |
| ISSN: | 1335-8871 |
| File URL: | fileadmin/internet/fachabteilungen/abteilung_8/8.4_mathematische_modellierung/Publikationen_8.4/epjconf_icm2014_00003.pdf |
| Web URL: | http://www.degruyter.com/view/j/msr.2014.14.issue-2/msr-2014-0009/msr-2014-0009.xml |
| Keywords: | dynamic measurement, acceleration, dynamic calibration, mixed model, design of experiment |
| Tags: | 8.42, Dynamik, Unsicherheit |
| Abstract: | In Annex H.5, the Guide to the Evaluation of Uncertainty in Measurement (GUM) [1] recognizes the necessity to analyze certain types of experiments by applying random effects ANOVA models. These belong to the more general family of linear mixed models that we focus on in the current paper. Extending the short introduction provided by the GUM, our aim is to show that the more general, linear mixed models cover a wider range of situations occurring in practice and can be beneficial when employed in data analysis of long-term repeated experiments. Namely, we point out their potential as an aid in establishing an uncertainty budget and as means for gaining more insight into the measurement process. We also comment on computational issues and to make the explanations less abstract, we illustrate all the concepts with the help of a measurement campaign conducted in order to challenge the uncertainty budget in calibration of accelerometers. |