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001			279601
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007			cr nn 008mamaa
008			150903s2008 xxu| o |||| 0|eng d
020			_a9780387741017 _99780387741017
024	7		_a10.1007/9780387741017 _2doi
035			_avtls000332462
039		9	_a201509030226 _bVLOAD _c201404122142 _dVLOAD _c201404091912 _dVLOAD _y201402041029 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA276-280
100	1		_aKjærulff, Uffe B. _eautor _9304019
245	1	0	_aBayesian Networks and Influence Diagrams : _bA Guide to Construction and Analysis / _cby Uffe B. Kjærulff, Anders L. Madsen.
264		1	_aNew York, NY : _bSpringer New York, _c2008.
300			_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		_aInformation Science and Statistics, _x1613-9011
500			_aSpringer eBooks
505	0		_aFundamentals -- Networks -- Probabilities -- Probabilistic Networks -- Solving Probabilistic Networks -- Model Construction -- Eliciting the Model -- Modeling Techniques -- Data-Driven Modeling -- Model Analysis -- Conflict Analysis -- Sensitivity Analysis -- Value of Information Analysis.
520			_aProbabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, prediction, decision making, classification, troubleshooting, and data mining under uncertainty. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis provides a comprehensive guide for practitioners who wish to understand, construct, and analyze intelligent systems for decision support based on probabilistic networks. Intended primarily for practitioners, this book does not require sophisticated mathematical skills or deep understanding of the underlying theory and methods nor does it discuss alternative technologies for reasoning under uncertainty. The theory and methods presented are illustrated through more than 140 examples, and exercises are included for the reader to check his/her level of understanding. The techniques and methods presented for knowledge elicitation, model construction and verification, modeling techniques and tricks, learning models from data, and analyses of models have all been developed and refined on the basis of numerous courses that the authors have held for practitioners worldwide. Uffe B. Kjærulff holds a PhD on probabilistic networks and is an Associate Professor of Computer Science at Aalborg University. Anders L. Madsen holds a PhD on probabilistic networks and is the CEO of HUGIN Expert A/S.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aMadsen, Anders L. _eautor _9304020
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9780387741000
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-74101-7 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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999			_c279601 _d279601