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International Journal for Uncertainty Quantification

Erscheint 6 Ausgaben pro Jahr

ISSN Druckformat: 2152-5080

ISSN Online: 2152-5099

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 1.7 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.9 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.5 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.0007 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.5 SJR: 0.584 SNIP: 0.676 CiteScore™:: 3 H-Index: 25

Indexed in

CONSTRUCTION OF EVIDENCE BODIES FROM UNCERTAIN OBSERVATIONS

Volumen 6, Ausgabe 2, 2016, pp. 157-165
DOI: 10.1615/Int.J.UncertaintyQuantification.2016016572
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ABSTRAKT

The construction of evidence bodies is a key issue when the evidence theory is applied in uncertainty quantification. The existing approaches proposed for this topic are usually too subjective to obtain rational evidence bodies in the situation of uncertain observations. This paper introduces a repeated kernel-density-estimation based approach for constructing evidence bodies from uncertain observations. The typical uncertain observations-limited point measurements together with interval measurements are considered in this paper. Using kernel density estimation with a loop, a family of probability distribution about the given observations is obtained, the probability box characterized by the bounds of the probability distribution family is discretized to evidence bodies by an outer discretization method. The approach also considers the uncertainty in the distribution assumption during the kernel density estimation. A numerical example is used to demonstrate the proposed approach.

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