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Normalization-Invariant Fuzzy Logic Operations Explain Empirical Success of Student Distributions in Describing Measurement Uncertainty

机译:归一化 - 不变模糊逻辑运算解释学生分布描述测量不确定度的经验成功

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摘要

In engineering practice, usually measurement errors are described by normal distributions. However, in some cases, the distribution is heavy-tailed and thus, not normal. In such situations, empirical evidence shows that the Student distributions are most adequate. The corresponding recommendation -- based on empirical evidence -- is included in the International Organization for Standardization guide. In this paper, we explain this empirical fact by showing that a natural fuzzy-logic-based formalization of commonsense requirements leads exactly to the Student\u27s distributions.
机译:在工程实践中,通常用正态分布描述测量误差。但是,在某些情况下,分布是重尾的,因此不正常。在这种情况下,经验证据表明学生分布最充分。相应的建议-基于经验证据-包含在《国际标准化组织指南》中。在本文中,我们通过证明基于自然模糊逻辑的常识要求的形式化正好导致了Student分布,从而解释了这一经验事实。

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