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Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/58589
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dc.contributor.authorMichael Beeren_US
dc.contributor.authorZitong Gongen_US
dc.contributor.authorIngo Neumannen_US
dc.contributor.authorSongsak Sriboonchittaen_US
dc.contributor.authorVladik Kreinovichen_US
dc.date.accessioned2018-09-05T04:26:34Z-
dc.date.available2018-09-05T04:26:34Z-
dc.date.issued2018-01-01en_US
dc.identifier.issn1860949Xen_US
dc.identifier.other2-s2.0-85038842719en_US
dc.identifier.other10.1007/978-3-319-73150-6_5en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85038842719&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/58589-
dc.description.abstract© 2018, Springer International Publishing AG. It is well know how to estimate the uncertainty of the result y of data processing if we know the correlations between all the inputs. Sometimes, however, we have no information about the correlations. In this case, instead of a single value σ of the standard deviation of the result, we get a range [σ̲,σ¯] of possible values. In this paper, we show how to compute this range.en_US
dc.subjectComputer Scienceen_US
dc.titleWhat if we do not know correlations?en_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume760en_US
article.stream.affiliationsGottfried Wilhelm Leibniz Universitaten_US
article.stream.affiliationsUniversity of Liverpoolen_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsUniversity of Texas at El Pasoen_US
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