Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/57545
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dc.contributor.authorSongsak Sriboonchittaen_US
dc.contributor.authorVladik Kreinovichen_US
dc.date.accessioned2018-09-05T03:45:27Z-
dc.date.available2018-09-05T03:45:27Z-
dc.date.issued2017-01-01en_US
dc.identifier.issn16860209en_US
dc.identifier.other2-s2.0-85039731776en_US
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85039731776&origin=inwarden_US
dc.identifier.urihttp://cmuir.cmu.ac.th/jspui/handle/6653943832/57545-
dc.description.abstract© 2017 by the Mathematical Association of Thailand. All rights reserved. In many practical situations, the Maximum Entropy (MaxEnt) approach leads to reasonable distributions. However, in an important case when all we know is that the value of a random variable is somewhere within the interval, this approach leads to a uniform distribution on this interval – while our intuition says that we should have a distribution whose probability density tends to 0 when we approach the interval’s endpoints. In this paper, we show that in most cases of interval uncertainty, we have additional information, and if we account for this additional information when applying MaxEnt, we get distributions which are in perfect accordance with our intuition.en_US
dc.subjectMathematicsen_US
dc.titleHow to get beyond uniform when applying Maxent to interval uncertaintyen_US
dc.typeJournalen_US
article.title.sourcetitleThai Journal of Mathematicsen_US
article.volume15en_US
article.stream.affiliationsChiang Mai Universityen_US
article.stream.affiliationsUniversity of Texas at El Pasoen_US
Appears in Collections:CMUL: Journal Articles

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