Da: The Book Escape, Baltimore, MD, U.S.A.
EUR 64,28
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Aggiungi al carrelloHardcover. Condizione: Good. 2nd Edition. Light pencil underlining in a few sections of text. Could be erased if one desired. ***Shipped within 24 hours from the beautiful Baltimore inner harbor area. First class service; accurate descriptions. Most items packed in boxes, not envelopes.***. Book.
Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 92,92
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Da: Chiron Media, Wallingford, Regno Unito
EUR 86,35
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Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 105,23
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Da: Ria Christie Collections, Uxbridge, Regno Unito
EUR 130,66
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EUR 124,03
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EUR 134,24
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Da: California Books, Miami, FL, U.S.A.
EUR 138,38
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EUR 130,65
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EUR 132,72
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Aggiungi al carrelloBuch. Condizione: Neu. Druck auf Anfrage Neuware - Printed after ordering - Probabilistic 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.
EUR 139,74
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Aggiungi al carrelloGebunden. Condizione: New. Gives a well-founded practical introduction to Bayesian networksIncludes presentation of the most efficient algorithm for solving influence diagramsThis is a brand new edition of an essential work on Bayesian networks and decision graph.
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 95,13
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EUR 156,03
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Da: Mispah books, Redhill, SURRE, Regno Unito
EUR 146,36
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Aggiungi al carrelloPaperback. Condizione: Like New. Like New. book.
Da: BennettBooksLtd, North Las Vegas, NV, U.S.A.
EUR 136,94
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Aggiungi al carrellohardcover. Condizione: New. In shrink wrap. Looks like an interesting title!
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
EUR 122,84
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EUR 178,62
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EUR 201,99
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EUR 226,49
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Da: moluna, Greven, Germania
EUR 77,17
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Gives a well-founded practical introduction to Bayesian networksIncludes presentation of the most efficient algorithm for solving influence diagramsThis is a brand new edition of an essential work on Bayesian networks and decision graph.
Editore: Springer New York Jun 2007, 2007
ISBN 10: 0387682813 ISBN 13: 9780387682815
Lingua: Inglese
Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germania
EUR 128,39
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Aggiungi al carrelloBuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Probabilistic 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. 447 pp. Englisch.
Da: Majestic Books, Hounslow, Regno Unito
EUR 135,60
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Aggiungi al carrelloCondizione: New. Print on Demand pp. 464.
Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 141,61
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Aggiungi al carrelloCondizione: New. PRINT ON DEMAND pp. 464.