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Hardcover. Condition: Good. No Jacket. Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 1.6.
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Bayesian Networks and Decision Graphs (Information Science and Statistics)
Seller: Goodwill of Greater Milwaukee and Chicago, Racine, WI, U.S.A.
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Condition: good. Book is considered to be in good or better condition. The actual cover image may not match the stock photo. Hard cover books may show signs of wear on the spine, cover or dust jacket. Paperback book may show signs of wear on spine or cover as well as having a slight bend, curve or creasing to it. Book should have minimal to no writing inside and no highlighting. Pages should be free of tears or creasing. Stickers should not be present on cover or elsewhere, and any CD or DVD expected with the book is included. Book is not a former library copy.
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Add to basketCondition: 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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Hardcover. 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 (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
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Add to basketOriginal 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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Condition: New. pp. 464.
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hardcover. Condition: New. In shrink wrap. Looks like an interesting title!
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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
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Bayesian Networks and Decision Graphs
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Condition: New. pp. 468 2nd Edition.
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Bayesian Networks and Decision Graphs
Published by Springer-Verlag New York Inc., US, 2007
ISBN 10: 0387682813 ISBN 13: 9780387682815
Language: English
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Add to basketHardback. Condition: New. 2nd ed. 2007. 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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US$ 165.64
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Add to basketCondition: 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.
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US$ 162.61
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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.
-
Bayesian Networks and Decision Graphs
Published by Springer-Verlag New York Inc., US, 2007
ISBN 10: 0387682813 ISBN 13: 9780387682815
Language: English
US$ 207.24
Convert currencyUS$ 87.52 shipping from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHardback. Condition: New. 2nd ed. 2007. 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.
