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TU Delft expert judgment data base

机译:代尔夫特理工大学专家判断数据库

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We review the applications of structured expert judgment uncertainty quantification using the "classical model" developed at the Delft University of Technology over the last 17 years [Cooke RM. Experts in uncertainty. Oxford: Oxford University Press; 1991; Expert judgment study on atmospheric dispersion and deposition. Report Faculty of Technical Mathematics and Informatics No.01-81, Delft University of Technology; 1991]. These involve 45 expert panels, performed under contract with problem owners who reviewed and approved the results. With a few exceptions, all these applications involved the use of seed variables; that is, variables from the experts' area of expertise for which the true values are available post hoc. Seed variables are used to (1) measure expert performance, (2) enable performance-based weighted combination of experts' distributions, and (3) evaluate and hopefully validate the resulting combination or "decision maker". This article reviews the classical model for structured expert judgment and the performance measures, reviews applications, comparing performance-based decision makers with "equal weight" decision makers, and collects some lessons learned.
机译:我们回顾了过去17年在代尔夫特理工大学开发的“经典模型”对结构化专家判断不确定性量化的应用。不确定性方面的专家。牛津:牛津大学出版社; 1991年;关于大气扩散和沉积的专家判断研究。代尔夫特理工大学技术数学和信息学报告学院No.01-81; 1991]。其中包括45个专家小组,与问题所有者签署合同,由他们审查和批准结果。除少数例外,所有这些应用程序都涉及种子变量的使用。也就是说,来自专家专业领域的变量可以事后获得真实值。种子变量用于(1)衡量专家的绩效,(2)启用基于绩效的专家分布的加权组合,以及(3)评估并希望验证结果组合或“决策制定者”。本文回顾了用于结构化专家判断和绩效度量的经典模型,回顾了应用程序,将基于绩效的决策者与“等权”决策者进行了比较,并收集了一些经验教训。

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