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  • When proposing and processing uncertainty decision making algorithms of various kinds and purposes we meet more and more often probability distributions ascribing to random events non-numerical uncertainty degrees. The reason is that we have to process systems of uncertainties for which the classical conditions like sigma-additivity or linear ordering of values are too restrictive to define sufficiently closely the nature of uncertainty we would like to specify and process. For the case of non-numerical uncertainty degrees at least the two criteria may be considered. First systems with rather complicated, but sophisticated and nontrivially formally analyzable uncertainty degrees. E.g., uncertainties supported by some algebras or partially ordered structures. Contrary, we may consider more easy non-numerical, but on the intuitive level interpretable relations. Well-known examples of such structures are set-valued possibilistic measures. Some perhaps interesting particular results in this direction will be introduced and analyzed in the contribution.
  • When proposing and processing uncertainty decision making algorithms of various kinds and purposes we meet more and more often probability distributions ascribing to random events non-numerical uncertainty degrees. The reason is that we have to process systems of uncertainties for which the classical conditions like sigma-additivity or linear ordering of values are too restrictive to define sufficiently closely the nature of uncertainty we would like to specify and process. For the case of non-numerical uncertainty degrees at least the two criteria may be considered. First systems with rather complicated, but sophisticated and nontrivially formally analyzable uncertainty degrees. E.g., uncertainties supported by some algebras or partially ordered structures. Contrary, we may consider more easy non-numerical, but on the intuitive level interpretable relations. Well-known examples of such structures are set-valued possibilistic measures. Some perhaps interesting particular results in this direction will be introduced and analyzed in the contribution. (en)
Title
  • Some results on set-valued possibilistic distributions
  • Some results on set-valued possibilistic distributions (en)
skos:prefLabel
  • Some results on set-valued possibilistic distributions
  • Some results on set-valued possibilistic distributions (en)
skos:notation
  • RIV/67985807:_____/12:00381402!RIV13-GA0-67985807
http://linked.open...avai/riv/aktivita
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  • I, P(GAP202/10/1826)
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  • 169236
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  • RIV/67985807:_____/12:00381402
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  • probability measures; possibility measures; non-numerical uncertainty degrees; set-valued uncertainty degrees; possibilistic uncertainty and set-valued entropy functions (en)
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  • [EC671CEB119C]
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  • Mariánské Lázně
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  • Prague
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  • Proceedings of 9th Workshop on Uncertainty Processing
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  • Kramosil, Ivan
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http://linked.open.../riv/zahajeniAkce
number of pages
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  • Faculty of Management, University of Economics
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  • 978-80-245-1885-5
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