000			03558nam a22005175i 4500
001			978-3-642-24905-1
003			DE-He213
005			20140220083304.0
007			cr nn 008mamaa
008			111116s2012 gw | s |||| 0|eng d
020			_a9783642249051 _9978-3-642-24905-1
024	7		_a10.1007/978-3-642-24905-1 _2doi
050		4	_aTA329-348
050		4	_aTA640-643
072		7	_aTBJ _2bicssc
072		7	_aMAT003000 _2bisacsh
082	0	4	_a519 _223
100	1		_aNguyen, Hung T. _eauthor.
245	1	0	_aComputing Statistics under Interval and Fuzzy Uncertainty _h[electronic resource] : _bApplications to Computer Science and Engineering / _cby Hung T. Nguyen, Vladik Kreinovich, Berlin Wu, Gang Xiang.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2012.
300			_aXII, 432 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aStudies in Computational Intelligence, _x1860-949X ; _v393
505	0		_aPart I Computing Statistics under Interval and Fuzzy Uncertainty: Formulation of the Problem and an Overview of General Techniques Which Can Be Used for Solving this Problem -- Part II Algorithms for Computing Statistics Under Interval and Fuzzy Uncertainty -- Part III Towards Computing Statistics under Interval and Fuzzy Uncertainty: Gauging the Quality of the Input Data -- Part IV Applications -- Part V Beyond Interval and Fuzzy Uncertainty.
520			_aIn many practical situations, we are interested in statistics characterizing a population of objects: e.g. in the mean height of people from a certain area.   Most algorithms for estimating such statistics assume that the sample values are exact. In practice, sample values come from measurements, and measurements are never absolutely accurate. Sometimes, we know the exact probability distribution of the measurement inaccuracy, but often, we only know the upper bound on this inaccuracy. In this case, we have interval uncertainty: e.g. if the measured value is 1.0, and inaccuracy is bounded by 0.1, then the actual (unknown) value of the quantity can be anywhere between 1.0 - 0.1 = 0.9 and 1.0 + 0.1 = 1.1. In other cases, the values are expert estimates, and we only have fuzzy information about the estimation inaccuracy.   This book shows how to compute statistics under such interval and fuzzy uncertainty. The resulting methods are applied to computer science (optimal scheduling of different processors), to information technology (maintaining privacy), to computer engineering (design of computer chips), and to data processing in geosciences, radar imaging, and structural mechanics.
650		0	_aEngineering.
650		0	_aArtificial intelligence.
650		0	_aEngineering mathematics.
650	1	4	_aEngineering.
650	2	4	_aAppl.Mathematics/Computational Methods of Engineering.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
700	1		_aKreinovich, Vladik. _eauthor.
700	1		_aWu, Berlin. _eauthor.
700	1		_aXiang, Gang. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642249044
830		0	_aStudies in Computational Intelligence, _x1860-949X ; _v393
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-642-24905-1
912			_aZDB-2-ENG
999			_c102357 _d102357