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008			220601s2019 sz | o |||| 0|eng d
020			_a9783031015830
024	7		_a10.1007/978-3-031-01583-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA279.5 _b2019 EB
100	1		_aDechter, Rina _d1950- _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686159
245	1	0	_aReasoning with Probabilistic and Deterministic Graphical Models : _bExact Algorithms _cby Rina Dechter
250			_a1st edition 2019
250			_aSecond edition
264		1	_aCham _bSpringer International Publishing _c2019
300			_a1 recurso en línea (XIV, 185 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Artificial Intelligence and Machine Learning _x1939-4616
505	0		_aPreface -- Introduction -- Defining Graphical Models -- Inference: Bucket Elimination for Deterministic Networks -- Inference: Bucket Elimination for Probabilistic Networks -- Tree-Clustering Schemes -- AND/OR Search Spaces for Graphical Models -- Combining Search and Inference: Trading Space for Time -- Conclusion -- Bibliography -- Author's Biography.
520			_aGraphical models (e.g., Bayesian and constraint networks, influence diagrams, and Markov decision processes) have become a central paradigm for knowledge representation and reasoning in both artificial intelligence and computer science in general. These models are used to perform many reasoning tasks, such as scheduling, planning and learning, diagnosis and prediction, design, hardware and software verification, and bioinformatics. These problems can be stated as the formal tasks of constraint satisfaction and satisfiability, combinatorial optimization, and probabilistic inference. It is well known that the tasks are computationally hard, but research during the past three decades has yielded a variety of principles and techniques that significantly advanced the state of the art. This book provides comprehensive coverage of the primary exact algorithms for reasoning with such models. The main feature exploited by the algorithms is the model's graph. We present inference-based, message-passing schemes (e.g., variable-elimination) and search-based, conditioning schemes (e.g., cycle-cutset conditioning and AND/OR search). Each class possesses distinguished characteristics and in particular has different time vs. space behavior. We emphasize the dependence of both schemes on few graph parameters such as the treewidth, cycle-cutset, and (the pseudo-tree) height. The new edition includes the notion of influence diagrams, which focus on sequential decision making under uncertainty. We believe the principles outlined in the book would serve well in moving forward to approximation and anytime-based schemes. The target audience of this book is researchers and students in the artificial intelligence and machine learning area, and beyond.
988			_aSynthesis Collection of Technology_2019
650		7	_2embne _9160470 _aEstadística bayesiana
650		7	_2embne _9152774 _aToma de decisiones (Estadística)
776	0	8	_iPrinted edition: _z9783031000287
776	0	8	_iPrinted edition: _z9783031004551
776	0	8	_iPrinted edition: _z9783031027116
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-01583-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b01/2023 _dz _eb _zSI