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Tutorial and spreadsheets for Bayesian evaluation of risks of false decisions on conformity of a multicomponent material or object due to measurement uncertainty

机译:由于测量不确定性导致多组分材料或物体的符合性的虚假决定风险的教程和电子表格

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摘要

A tutorial and a user-friendly program for evaluating risks of false decisions in conformity assessment of a multicomponent material or object due to measurement uncertainty, based on a Bayesian approach, are presented. The developed program consists of two separate MS-Excel spreadsheets. It allows calculation of the consumer's and producer's risks concerning each component of the material whose concentration was tested ('particular risks') as well as concerning the material as a whole ('total risks'). According to the Bayesian framework, probability density functions of the actual/'true' component concentrations (prior pdfs) and likelihood functions (likelihoods) of the corresponding test results are used to model the knowledge about the material or object. Both cases of independent and correlated variables (the actual concentrations and the test results) are treated in the present work. Spreadsheets provide an estimate of the joint posterior pdf for the actual component concentrations as the normalized product of the multivariate prior pdf and the likelihood, starting from normal or log-normal prior pdfs and normal likelihoods, using Markov chain Monte Carlo (MCMC) simulations by the Metropolis-Hastings algorithm. The principles of Bayesian inference and MCMC are described for users with basic knowledge in statistics, necessary for correct formulation of a task and interpretation of the calculation results. The spreadsheet program was validated by comparison of the obtained results with analytical results calculated in the R programming environment. The developed program allows estimation of risks greater than 0.003% with standard deviations of such estimates spreading from 0.001% to 1.5%, depending on the risk value. Such estimation characteristics are satisfactory, taking into account known variability in measurement uncertainty associated with the test results of multicomponent materials.
机译:提出了一种针对贝叶斯方法的测量不确定性,用于评估用于评估伪决定的风险的教程和用户友好的程序,这是基于贝叶斯方法的测量不确定性。开发的程序包括两个单独的MS-Excel电子表格。它允许计算消费者和生产者关于所测试浓度(“特定风险”)以及整体材料(“总风险”)的材料的风险的风险。根据贝叶斯框架,实际/“真实”组件浓度(先前PDF)的概率密度函数和相应测试结果的似然函数(似然)用于建模关于材料或物体的知识。两种独立和相关变量(实际浓度和测试结果)在本作中处理。电子表格为实际组分浓度的关节后面PDF作为多变量的PDF的标准化产物和似的估计,从正常或逻辑正常的PDF和正常似然开始,使用Markov Chain Monte Carlo(MCMC)模拟开始Metropolis-Hastings算法。贝叶斯推理和MCMC的原则被描述为具有基本知识的统计知识,正确制定任务和计算结果的解释所必需的。通过在R编程环境中计算的分析结果进行验证,验证了电子表格程序。开发的程序允许估计大于0.003%的风险,这些估计的标准差距从0.001%达到1.5%,这取决于风险值。这种估计特征是令人满意的,考虑到与多组分材料的测试结果相关的测量不确定性的已知变异。

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