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000724482 019__ $$a899563509$$a908087625
000724482 020__ $$a9783319126289$$qelectronic book
000724482 020__ $$a3319126288$$qelectronic book
000724482 020__ $$z9783319126272
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000724482 0247_ $$a10.1007/978-3-319-12628-9$$2doi
000724482 035__ $$aSP(OCoLC)ocn896824765
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000724482 049__ $$aISEA
000724482 050_4 $$aQ375
000724482 08204 $$a003.54$$223
000724482 1001_ $$aServin, Christian,$$eauthor.
000724482 24510 $$aPropagation of interval and probabilistic uncertainty in cyberinfrastructure-related data processing and data fusion$$h[electronic resource] /$$cChristian Servin, Vladik Kreinovich.
000724482 264_1 $$aCham :$$bSpringer,$$c[2015]
000724482 264_4 $$c©2015
000724482 300__ $$a1 online resource :$$billustrations.
000724482 336__ $$atext$$btxt$$2rdacontent
000724482 337__ $$acomputer$$bc$$2rdamedia
000724482 338__ $$aonline resource$$bcr$$2rdacarrier
000724482 4901_ $$aStudies in systems, decision and control ;$$vvolume 15
000724482 504__ $$aIncludes bibliographical references and index.
000724482 5050_ $$aIntroduction -- Towards a More Adequate Description of Uncertainty -- Towards Justification of Heuristic Techniques for Processing Uncertainty -- Towards More Computationally Efficient Techniques for Processing Uncertainty -- Towards Better Ways of Extracting Information About Uncertainty from Data.
000724482 506__ $$aAccess limited to authorized users.
000724482 520__ $$aOn various examples ranging from geosciences to environmental sciences, this book explains how to generate an adequate description of uncertainty, how to justify semiheuristic algorithms for processing uncertainty, and how to make these algorithms more computationally efficient. It explains in what sense the existing approach to uncertainty as a combination of random and systematic components is only an approximation, presents a more adequate three-component model with an additional periodic error component, and explains how uncertainty propagation techniques can be extended to this model. The book provides a justification for a practically efficient heuristic technique (based on fuzzy decision-making). It explains how the computational complexity of uncertainty processing can be reduced. The book also shows how to take into account that in real life, the information about uncertainty is often only partially known, and, on several practical examples, explains how to extract the missing information about uncertainty from the available data.
000724482 588__ $$aDescription based on online resource; title from PDF title page (viewed December 2, 2014).
000724482 650_0 $$aUncertainty (Information theory)
000724482 650_0 $$aCyberinfrastructure.
000724482 7001_ $$aKreinovich, Vladik,$$eauthor.
000724482 77608 $$iPrint version:$$z331912627X$$z9783319126272
000724482 830_0 $$aStudies in systems. decision and control ;$$vv. 15.
000724482 852__ $$bebk
000724482 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-12628-9$$zOnline Access$$91397441.1
000724482 909CO $$ooai:library.usi.edu:724482$$pGLOBAL_SET
000724482 980__ $$aEBOOK
000724482 980__ $$aBIB
000724482 982__ $$aEbook
000724482 983__ $$aOnline
000724482 994__ $$a92$$bISE