Articles liés à Bayesian Networks and Decision Graphs
Synopsis
This is a brand 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. The reader is guided through the two types of frameworks with examples and exercises, which also give instruction on how to build these models. Structured in two parts, the first section focuses on probabilistic graphical models, while the second part deals with decision graphs, and in addition to the frameworks described in the previous edition, also introduces Markov decision process. 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.
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Bayesian Networks and Decision Graphs
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Springer, 2007
ISBN 10 : 0387682813
ISBN 13 : 9780387682815
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Bayesian Networks and Decision Graphs (Information Science and Statistics)
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Springer, 2007
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ISBN 13 : 9780387682815
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Bayesian Networks and Decision Graphs (Information Science and Statistics)
Edité par
Springer, 2007
ISBN 10 : 0387682813
ISBN 13 : 9780387682815
Ancien ou d'occasion
Couverture rigide
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Bayesian Networks and Decision Graphs (Information Science and Statistics)
Edité par
Springer, 2007
ISBN 10 : 0387682813
ISBN 13 : 9780387682815
Ancien ou d'occasion
Couverture rigide
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Hardcover. Etat : 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. N° de réf. du vendeur 000263
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Bayesian Networks and Decision Graphs
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Springer, 2007
ISBN 10 : 0387682813
ISBN 13 : 9780387682815
Ancien ou d'occasion
Couverture rigide
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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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Springer New York Jun 2007, 2007
ISBN 10 : 0387682813
ISBN 13 : 9780387682815
Neuf
Couverture rigide
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
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Buch. Etat : 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. N° de réf. du vendeur 9780387682815
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Bayesian Networks and Decision Graphs
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Bayesian Networks and Decision Graphs (Information Science and Statistics)
Vendeur : California Books, Miami, FL, Etats-Unis
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Bayesian Networks and Decision Graphs
Edité par
Springer New York, 2007
ISBN 10 : 0387682813
ISBN 13 : 9780387682815
Neuf
Couverture rigide
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
Évaluation du vendeur 5 sur 5 étoiles
Buch. Etat : 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. N° de réf. du vendeur 9780387682815
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