000 03186nam a22005055i 4500
001 978-3-319-12628-9
003 DE-He213
005 20200421111700.0
007 cr nn 008mamaa
008 141120s2015 gw | s |||| 0|eng d
020 _a9783319126289
_9978-3-319-12628-9
024 7 _a10.1007/978-3-319-12628-9
_2doi
050 4 _aQ342
072 7 _aUYQ
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
100 1 _aServin, Christian.
_eauthor.
245 1 0 _aPropagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion
_h[electronic resource] /
_cby Christian Servin, Vladik Kreinovich.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2015.
300 _aVIII, 112 p. 22 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Systems, Decision and Control,
_x2198-4182 ;
_v15
505 0 _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.
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.
650 0 _aEngineering.
650 0 _aData mining.
650 0 _aStatistics.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aComputational Intelligence.
650 2 4 _aData Mining and Knowledge Discovery.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
700 1 _aKreinovich, Vladik.
_eauthor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319126272
830 0 _aStudies in Systems, Decision and Control,
_x2198-4182 ;
_v15
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-12628-9
912 _aZDB-2-ENG
942 _cEBK
999 _c54936
_d54936