LEADER 03864nam 22006855 450 001 9910299852403321 005 20251113195303.0 010 $a3-319-12628-8 024 7 $a10.1007/978-3-319-12628-9 035 $a(CKB)3710000000291498 035 $a(EBL)1967676 035 $a(OCoLC)896824765 035 $a(SSID)ssj0001386273 035 $a(PQKBManifestationID)11814640 035 $a(PQKBTitleCode)TC0001386273 035 $a(PQKBWorkID)11349578 035 $a(PQKB)10200945 035 $a(DE-He213)978-3-319-12628-9 035 $a(MiAaPQ)EBC1967676 035 $a(PPN)18309378X 035 $a(EXLCZ)993710000000291498 100 $a20141120d2015 u| 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aPropagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion /$fby Christian Servin, Vladik Kreinovich 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (117 p.) 225 1 $aStudies in Systems, Decision and Control,$x2198-4190 ;$v15 300 $aDescription based upon print version of record. 311 08$a3-319-12627-X 320 $aIncludes bibliographical references and index. 327 $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. 330 $aOn various examples ranging from geosciences to environmental sciences, this book explains how to generate an adequate description of uncertainty, how to justify semiheuristic algorithms for processing uncertainty, and how to make these algorithms more computationally efficient. It explains in what sense the existing approach to uncertainty as a combination of random and systematic components is only an approximation, presents a more adequate three-component model with an additional periodic error component, and explains how uncertainty propagation techniques can be extended to this model. The book provides a justification for a practically efficient heuristic technique (based on fuzzy decision-making). It explains how the computational complexity of uncertainty processing can be reduced. The book also shows how to take into account that in real life, the information about uncertainty is often only partially known, and, on several practical examples, explains how to extract the missing information about uncertainty from the available data. 410 0$aStudies in Systems, Decision and Control,$x2198-4190 ;$v15 606 $aComputational intelligence 606 $aData mining 606 $aStatistics 606 $aComputational Intelligence 606 $aData Mining and Knowledge Discovery 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 615 0$aComputational intelligence. 615 0$aData mining. 615 0$aStatistics. 615 14$aComputational Intelligence. 615 24$aData Mining and Knowledge Discovery. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 676 $a003.54 700 $aServin$b Christian$4aut$4http://id.loc.gov/vocabulary/relators/aut$0720973 702 $aKreinovich$b Vladik$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910299852403321 996 $aPropagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion$92527390 997 $aUNINA