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Bayesian statistics for determination of the reference value and degree of equivalence of inconsistent comparison data

机译:贝叶斯统计量用于确定不一致的比较数据的参考值和等价程度

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摘要

Three methods for the determination of the key comparison reference value (KCRV) and degree of equivalence of inconsistent comparison data are proposed in this study. These methods are, respectively, based on the premises of (1) unknown biases of individual measurement values, (2) underestimated uncertainties of individual participants or (3) additional and common uncertainty. Bayesian statistics were employed for the analysis using locally uniform priors. In the case of the first premise, Procedure B in the CIPM guidelines (2002 Metrologia 39 589-95) can be derived in the Bayesian context. In the case of the second and the third premises, the weighted mean is a possible candidate for the KCRV. These methods are exemplified using the key comparison data of CIPM CCM.FF-K3 and APMP.L-K1. Markov chain Monte Carlo simulations were conducted for calculations based on the latter two premises. From the results obtained, it is considered that, in addition to Procedure B in the CIPM guidelines, the method based on the second premise is also a robust method for the estimation of the KCRV.
机译:本文提出了三种确定关键比较参考值(KCRV)和比较数据不一致的等效程度的方法。这些方法分别基于以下前提:(1)单个测量值的未知偏差;(2)单个参与者的不确定性被低估了;或(3)其他和常见的不确定性。使用局部一致先验的贝叶斯统计量进行分析。在第一个前提的情况下,CIPM指南(2002 Metrologia 39 589-95)中的过程B可以在贝叶斯上下文中得出。在第二和第三前提的情况下,加权均值可能是KCRV的候选者。这些方法以CIPM CCM.FF-K3和APMP.L-K1的关键比较数据为例。基于后两个前提进行了马尔可夫链蒙特卡罗模拟计算。根据获得的结果,可以认为,除了CIPM指南中的程序B之外,基于第二前提的方法对于KCRV的估算也是一种可靠的方法。

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