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007			cr nn 008mamaa
008			141120s2015 gw | s |||| 0|eng d
020			_a9783319126289
024	7		_a10.1007/978-3-319-12628-9 _2doi
040			_bspa _dES-MaUEC
050		4	_aQ375 _b.S478 2015 EB
100	1		_aServin, Christian. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/127091604/
245	1	0	_aPropagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion _cby Christian Servin, Vladik Kreinovich.
264		1	_aCham _bSpringer International Publishing _c2015
300			_a1 recurso en línea (VIII, 112 páginas 22 ilustraciones)
336			_2rdacontent _aTexto (visual) _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
490	0		_aStudies in Systems, Decision and Control, _x2198-4182 ; _v15
490	0		_aEngineering (Springer-11647)
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	3		_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.
988			_aEBSPRINGER_2018
650		7	_aIncertidumbre (Teoría de la información) _2embne _9667868
700	1		_aKreinovich, Vladik _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _0http://id.loc.gov/authorities/names/n95102697 _1http://viaf.org/viaf/37212054/ _998177
776	0	8	_iEdición impresa: _z9783319126296
776	0	8	_iEdición impresa: _z9783319126272
776	0	8	_iEdición impresa: _z9783319385877
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-319-12628-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2019 _dz _eIG _zSI