A Case Study for Integrating Comp/Sim Credibility and Convolved UQ and Evidence Theory Results to Support Risk Informed Decision Making

Orient, George Edgar; Babuška, Vít; Lo, David; Philip, Mersch, John; Wapman, W.

doi:10.1007/978-3-319-74793-4_25

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“…In DST, the probabilities are assigned to basic probability assignments (BPAs), which is presented to generalize the BPA in probability distribution in PT. The DST has shown its effectiveness and advantages in wide applications with uncertainty in terms of decision making, such as knowledge reasoning [ 7 , 8 , 9 ], sensor fusion [ 10 , 11 , 12 , 13 ], reliability analysis [ 14 , 15 ], fault diagnosis [ 16 , 17 , 18 ], assessment and evaluation [ 19 , 20 , 21 ], image recognition [ 22 , 23 ], and others [ 24 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel Belief Entropy for Measuring Uncertainty in Dempster-Shafer Evidence Theory Framework Based on Plausibility Transformation and Weighted Hartley Entropy

Pan

Zhou

Tang

et al. 2019

Entropy

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Dempster-Shafer evidence theory (DST) has shown its great advantages to tackle uncertainty in a wide variety of applications. However, how to quantify the information-based uncertainty of basic probability assignment (BPA) with belief entropy in DST framework is still an open issue. The main work of this study is to define a new belief entropy for measuring uncertainty of BPA. The proposed belief entropy has two components. The first component is based on the summation of the probability mass function (PMF) of single events contained in each BPA, which are obtained using plausibility transformation. The second component is the same as the weighted Hartley entropy. The two components could effectively measure the discord uncertainty and non-specificity uncertainty found in DST framework, respectively. The proposed belief entropy is proved to satisfy the majority of the desired properties for an uncertainty measure in DST framework. In addition, when BPA is probability distribution, the proposed method could degrade to Shannon entropy. The feasibility and superiority of the new belief entropy is verified according to the results of numerical experiments.

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