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dc.contributor.authorSongsak Sriboonchittaen_US
dc.contributor.authorHung T. Nguyenen_US
dc.contributor.authorVladik Kreinovichen_US
dc.contributor.authorOlga Koshelevaen_US
dc.description.abstract© Springer International Publishing AG 2017. Often, we only have partial knowledge about a probability distribution, and we would like to select a single probability distribution ρ(x) out of all probability distributions which are consistent with the available knowledge. One way to make this selection is to take into account that usually, the values x of the corresponding quantity are also known only with some accuracy. It is therefore desirable to select a distribution which is the most robust—in the sense the x-inaccuracy leads to the smallest possible inaccuracy in the resulting probabilities. In this paper, we describe the corresponding most robust probability distributions, and we show that the use of resulting probability distributions has an additional advantage: it makes related computations easier and faster.en_US
dc.subjectComputer Scienceen_US
dc.titleRobustness as a criterion for selecting a probability distribution under uncertaintyen_US
dc.typeBook Seriesen_US
article.title.sourcetitleStudies in Computational Intelligenceen_US
article.volume692en_US Mai Universityen_US Mexico State University Las Crucesen_US of Texas at El Pasoen_US
Appears in Collections:CMUL: Journal Articles

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