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Uncertainty assessment in high-risk environments using probability, evidence theory and expert judgment elicitation.

机译：使用概率，证据理论和专家判断启发对高风险环境中的不确定性进行评估。

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摘要

The level of uncertainty in advanced system design is assessed by comparing the results of expert judgment elicitation to probability and evidence theory. This research shows how one type of monotone measure, namely Dempster-Shafer Theory of Evidence can expand the framework of uncertainty to provide decision makers a more robust solution space. The issues imbedded in this research are focused on how the relevant predictive uncertainty produced by similar action is measured.;This methodology uses the established approach from traditional probability theory and Dempster-Shafer evidence theory to combine two classes of uncertainty, aleatory and epistemic. Probability theory provides the mathematical structure traditionally used in the representation of aleatory uncertainty. The uncertainty in analysis outcomes is represented by probability distributions and typically summarized as Complimentary Cumulative Distribution Functions (CCDFs). The main components of this research are probability of X in the probability theory compared to mx in evidence theory. Using this comparison, an epistemic model is developed to obtain the upper "CCPF - Complimentary Cumulative Plausibility Function" limits and the lower "CCBF - Complimentary Cumulative Belief Function" limits compared to the traditional probability function.;A conceptual design for the Thermal Protection System (TPS) of future Crew Exploration Vehicles (CEV) is used as an initial test case. A questionnaire is tailored to elicit judgment from experts in high-risk environments. Based on description and characteristics, the answers of the questionnaire produces information, that serves as qualitative semantics used for the evidence theory functions. The computational mechanism provides a heuristic approach for the compilation and presentation of the results. A follow-up evaluation serves as validation of the findings and provides useful information in terms of consistency and adoptability to other domains.;The results of this methodology provide a useful and practical approach in conceptual design to aid the decision maker in assessing the level of uncertainty of the experts. The methodology presented is well-suited for decision makers that encompass similar conceptual design instruments.
机译：通过将专家判断引发的结果与概率和证据理论进行比较，可以评估高级系统设计中的不确定性水平。这项研究表明，一种单调测度（即Dempster-Shafer证据理论）如何扩展不确定性的框架，从而为决策者提供更强大的解决方案空间。这项研究中的问题主要集中在如何测量类似动作产生的相关预测不确定性上。该方法使用传统概率论和Dempster-Shafer证据论中的既定方法，将偶然性和认识论两类不确定性结合起来。概率理论提供了传统上用于表示不确定性的数学结构。分析结果中的不确定性由概率分布表示，通常总结为互补累积分布函数（CCDF）。这项研究的主要内容是概率论中的X概率与证据论中的mx相比。通过这种比较，建立了一个认知模型，以获取与传统概率函数相比较高的“ CCPF-累积累积可信度函数”下限和较低的“ CCBF-累积累积可信度函数”下限。未来的乘员探索车（CEV）（TPS）被用作初始测试用例。量身定制的调查表旨在引起高风险环境中专家的判断。根据描述和特征，问卷的答案会产生信息，这些信息可作为用于证据理论功能的定性语义。计算机制为结果的汇编和表示提供了一种启发式方法。后续评估可作为对结果的确认，并提供有关其他领域的一致性和可采用性的有用信息。该方法的结果为概念设计提供了一种有用且实用的方法，以帮助决策者评估决策水平。专家们的不确定性。所介绍的方法非常适合包含类似概念设计工具的决策者。

著录项

  • 作者

    Bondi, Stella Barberis.;

  • 作者单位

    Old Dominion University.;

  • 授予单位 Old Dominion University.;
  • 学科 Statistics.
  • 学位 Ph.D.
  • 年度 2007
  • 页码 131 p.
  • 总页数 131
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 古生物学;
  • 关键词

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