000			04600nam a22004695i 4500
001			978-0-387-68282-2
003			DE-He213
005			20250710084006.0
007			cr nn 008mamaa
008			100301s2007 xxu| s |||| 0|eng d
020			_a9780387682822 _a99780387682822
024	7		_a10.1007/978-0-387-68282-2 _2doi
082	0	4	_a005.55 _223
100	1		_aJensen, Finn V. _eauthor.
245	1	0	_aBayesian Networks and Decision Graphs _h[recurso electrónico] : _bFebruary 8, 2007 / _cby Finn V. Jensen, Thomas D. Nielsen.
250			_aSecond Edition.
264		1	_aNew York, NY : _bSpringer New York, _c2007.
300			_aXVI, 447 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aInformation Science and Statistics, _x1613-9011
505	0		_aPrerequisites on Probability Theory -- Prerequisites on Probability Theory -- Probabilistic Graphical Models -- Causal and Bayesian Networks -- Building Models -- Belief Updating in Bayesian Networks -- Analysis Tools for Bayesian Networks -- Parameter estimation -- Learning the Structure of Bayesian Networks -- Bayesian Networks as Classifiers -- Decision Graphs -- Graphical Languages for Specification of Decision Problems -- Solution Methods for Decision Graphs -- Methods for Analyzing Decision Problems.
520			_aProbabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. The book is a new edition of Bayesian Networks and Decision Graphs by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes. give practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge. < give several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs. present a thorough introduction to state-of-the-art solution and analysis algorithms. The book is intended as a textbook, but it can also be used for self-study and as a reference book. Finn V. Jensen is a professor at the department of computer science at Aalborg University, Denmark. Thomas D. Nielsen is an associate professor at the same department.
650		0	_aCOMPUTER SCIENCE.
650		0	_aARTIFICIAL INTELLIGENCE.
650		0	_aSTATISTICS.
650	1	4	_aCOMPUTER SCIENCE.
650	2	4	_aPROBABILITY AND STATISTICS IN COMPUTER SCIENCE.
650	2	4	_aARTIFICIAL INTELLIGENCE (INCL. ROBOTICS).
650	2	4	_aSTATISTICS FOR ENGINEERING, PHYSICS, COMPUTER SCIENCE, CHEMISTRY & GEOSCIENCES.
700	1		_aNielsen, Thomas D. _eauthor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9780387682815
830		0	_aInformation Science and Statistics, _x1613-9011
856	4	0	_uhttp://dx.doi.org/10.1007/978-0-387-68282-2 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SCS
942			_2ddc _cER
999			_c57948 _d57948