EMUE-D1-2-BayesianMassCalibration

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.本示例针对标称质量为100 g的参考砝码R，对待校准砝码W的常规质量实施校准。该示例基于JCGM 101:2008中的示例拓展而来，本次将采用贝叶斯方法对测量过程开展评估。 贝叶斯方法与JCGM 101:2008中的蒙特卡洛方法（Monte Carlo Method, MCM）以及JCGM 100:2008中的不确定度传播定律（Law of Propagation of Uncertainty, LPU）有所不同，其将被测量的先验知识与校准过程中获取的实验数据相结合。基于该组合得到的联合后验概率密度函数，可获取被测量的量值及其覆盖区间。 本数据集包含的文件如下： - EMUEActivity113_MassCalibration.pdf：报告《贝叶斯方法应用于JCGM 101:2008中的质量校准示例》； - EMUEActivity113_MassCalibration.tex：用于编译生成EMUEActivity113_MassCalibration.pdf的LaTeX源文件； - Compendium.bib：参考文献书目文件； - conjugateBayesKnownV.pdf：报告中所用插图； - MCMvsBayesNI.pdf：报告中所用插图； - JCGM101_Mass_calibration_code.R：用于运行本示例报告相关代码的R语言脚本文件。