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008			150903s2013 xxu| o |||| 0|eng d
020			_a9781461460404 _99781461460404
024	7		_a10.1007/9781461460404 _2doi
035			_avtls000341802
039		9	_a201509030339 _bVLOAD _c201405050234 _dVLOAD _y201402061108 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA276-280
100	1		_aGrover, Jeff. _eautor _9317234
245	1	0	_aStrategic Economic Decision-Making : _bUsing Bayesian Belief Networks to Solve Complex Problems / _cby Jeff Grover.
264		1	_aNew York, NY : _bSpringer New York : _bImprint: Springer, _c2013.
300			_axI, 116 páginas 35 ilustraciones, 22 ilustraciones en color. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aSpringerBriefs in Statistics, _x2191-544X ; _v9
500			_aSpringer eBooks
505	0		_aStrategic Economic Decision Making: The Use of Bayesian Belief Networks (BBN) in Solving Complex Problems -- A Literature Review of Bayes’ Theorem and Bayesian Belief Networks (BBN) -- Statistical Properties of Bayes’ Theorem -- Bayes Belief Networks (BBN) Experimental Protocol -- Manufacturing Example -- Political Science Example -- Gambling Example -- Publicly Traded Company Default Example -- Insurance Risk Levels Example -- Acts of Terrorism Example -- Currency Wars Example -- College Entrance Exams Example -- Special Forces Assessment and Selection (SFAS) One-Stage Example -- Special Forces Assessment and Selection (SFAS) Two-Stage Example.
520			_aStrategic Economic Decision-Making: Using Bayesian Belief Networks to Solve Complex Problems is a quick primer on the topic that introduces readers to the basic complexities and nuances associated with learning Bayes’ theory and inverse probability for the first time. This brief is meant for non-statisticians who are unfamiliar with Bayes’ theorem, walking them through the theoretical phases of set and sample set selection, the axioms of probability, probability theory as it pertains to Bayes’ theorem, and posterior probabilities. All of these concepts are explained as they appear in the methodology of fitting a Bayes’ model, and upon completion of the text readers will be able to mathematically determine posterior probabilities of multiple independent nodes across any system available for study.  Very little has been published in the area of discrete Bayes’ theory, and this brief will appeal to non-statisticians conducting research in the fields of engineering, computing, life sciences, and social sciences.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781461460398
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-1-4614-6040-4 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c287830 _d287830