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Evidential Model Validation under Epistemic Uncertainty

机译：认知不确定性下的证据模型验证

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摘要

This paper proposes evidence theory based methods to both quantify the epistemic uncertainty and validate computational model. Three types of epistemic uncertainty concerning input model data, that is, sparse points, intervals, and probability distributions with uncertain parameters, are considered. Through the proposed methods, the given data will be described as corresponding probability distributions for uncertainty propagation in the computational model, thus, for the model validation. The proposed evidential model validation method is inspired by the idea of Bayesian hypothesis testing and Bayes factor, which compares the model predictions with the observed experimental data so as to assess the predictive capability of the model and help the decision making of model acceptance. Developed by the idea of Bayes factor, the frame of discernment of Dempster-Shafer evidence theory is constituted and the basic probability assignment (BPA) is determined. Because the proposed validation method is evidence based, the robustness of the result can be guaranteed, and the most evidence-supported hypothesis about the model testing will be favored by the BPA. The validity of proposed methods is illustrated through a numerical example.
机译：本文提出了基于证据理论的方法来量化认知不确定性和验证计算模型。考虑与输入模型数据有关的三种认知不确定性，即稀疏点，区间和具有不确定参数的概率分布。通过提出的方法，给定的数据将被描述为计算模型中不确定性传播的相应概率分布，从而用于模型验证。提出的证据模型验证方法受到贝叶斯假设检验和贝叶斯因子的启发，将模型预测与观察到的实验数据进行比较，以评估模型的预测能力并帮助模型接受决策。由贝叶斯因子的思想发展起来，构成了Dempster-Shafer证据理论的识别框架，并确定了基本概率分配（BPA）。由于所提出的验证方法是基于证据的，因此可以保证结果的鲁棒性，并且BPA将支持有关模型测试的大多数证据支持的假设。通过数值例子说明了所提出方法的有效性。

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  • 来源
    《Mathematical Problems in Engineering》 |2018年第2期|6789635.1-6789635.11|共11页
  • 作者

    Deng Wei; Lu Xi; Deng Yong;

  • 作者单位

    Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610054, Sichuan, Peoples R China;

    Southwest Univ, Sch Comp & Informat Sci, Chongqing 400715, Peoples R China;

    Univ Elect Sci & Technol China, Inst Fundamental & Frontier Sci, Chengdu 610054, Sichuan, Peoples R China;

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