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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: ZENODO
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EMUE-D1-2-BayesianMassCalibration

Authors: S. Demeyer; N. Fischer; M. G. Cox; A. M. H. van der Veen; O. Pellegrino; J. Sousa; A. Bošnjaković; +2 Authors

EMUE-D1-2-BayesianMassCalibration

Abstract

The report relies on the following publications: - BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP, and OIML. Supplement 1 to the 'Guide to the Expression of Uncertainty in Measurement' – Propagation of distributions using a Monte Carlo method, JCGM 101:2008. BIPM, 2008. - A. Gelman, J. B. Carlin, H. S. Stern, D. B. Dunson, A. Vehtari, and D. B. Rubin. Bayesian Data Analysis, 3rd edition. Chapman and Hall/CRC, 2013. - G.E.P. Box and G.C. Tiao. Bayesian Inference in Statistical Analysis. Wiley, 1992. - C. Elster. Bayesian uncertainty analysis compared with the application of the GUM and its supplements. Metrologia, 51:S159–S166, 2014. - BIPM, IEC, IFCC, ILAC, ISO, IUPAC, IUPAP, and OIML. Guide to the Expression of Uncertainty in Measurement, JCGM 100:2008, GUM 1995 with minor corrections. BIPM, 2008. - J. A. Sousa, A. B. Forbes, A. S. Ribeiro, P. M. Harris, F. Carvalho, and L. Bacelar. The evaluation of uncertainty in mass calibration: possible approaches in a comparison study. Journal of Physics: Conference Series, 459:012033, sep 2013. - S. Chib and E. Greenberg. Understanding the metropolis-hastings algorithm. The American Statistician, 49(4):327–335, 1995.

This example describes the calibration of a conventional mass of a weight W against a reference weight R with a nominal mass of 100 g. The example builds on that given in JCGM 101:2008. This time a Bayesian evaluation of the measurement is performed. A Bayesian approach differs from the Monte Carlo method (MCM) of JCGM 101:2008 and the law of propagation of uncertainty (LPU) in JCGM 100:2008 in that it combines prior knowledge about the measurand with the data obtained during calibration. From the joint posterior probability density function which is obtained from this combination, a value and a coverage interval for the measurand are obtained. Files contained in the dataset are: - EMUEActivity113_MassCalibration.pdf: report “Bayesian approach applied to the mass calibration example in JCGM 101:2008”; - EMUEActivity113_MassCalibration.tex: LaTeX source file to be compiled in order to produce EMUEActivity113_MassCalibration.pdf; - Compendium.bib: bibliography file; - conjugateBayesKnownV.pdf: image contained in the report; - MCMvsBayesNI.pdf: image contained in the report; - JCGM101_Mass_calibration_code.R : R code to run the example from the report.

Keywords

Bayesian statistics, measurement uncertainty, prior knowledge, calibration

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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