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Condition: Like New. Most items will be dispatched the same or the next working day. An apparently unread copy in perfect condition. Dust cover is intact with no nicks or tears. Spine has no signs of creasing. Pages are clean and not marred by notes or folds of any kind.
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Bayesian Networks and Decision Graphs (Information Science & Statistics)
Published by Springer, New York, Usa, 2007
ISBN 10: 0387682813 ISBN 13: 9780387682815
Language: English
Seller: Literary Cat Books, Machynlleth, Powys, WALES, United Kingdom
Association Member: IOBA
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Original boards. Condition: Fine+. Second Edition; First Impression. Original colour printed boards. Slight shelfwear. ; 23.4x15.5x2.5 cm; 447 pages.
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Bayesian Networks and Decision Graphs (Information Science and Statistics)
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Bayesian Networks and Decision Graphs
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Bayesian Networks and Decision Graphs (Information Science and Statistics)
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Add to basketHardcover. Condition: 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.
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Bayesian Networks and Decision Graphs
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Bayesian Networks and Decision Graphs
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Bayesian Networks and Decision Graphs (Information Science and Statistics)
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Bayesian Networks and Decision Graphs
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Bayesian Networks and Decision Graphs (Paperback)
Published by Springer-Verlag New York Inc., New York, NY, 2010
ISBN 10: 1441923942 ISBN 13: 9781441923943
Language: English
£ 83.40
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Add to basketPaperback. Condition: new. Paperback. 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. This is a new edition of an essential work on Bayesian networks and decision graphs. It is an introduction to probabilistic graphical models including Bayesian networks and influence diagrams. It presents a thorough introduction to state-of-the-art solution and analysis algorithms. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Bayesian Networks and Decision Graphs
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Add to basketBuch. Condition: 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.
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