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Synopsis
Bayesian networks and decision graphs are formal graphical languages for representation and communication of decision scenarios requiring reasoning under uncertainty. Their strengths are two-sided. It is easy for humans to construct and to understand them, and when communicated to a computer, they can easily be compiled. Furthermore, handy algorithms are developed for analyses of the models and for providing responses to a wide range of requests such as belief updating, determining optimal strategies, conflict analyses of evidence, and most probable explanation. The book emphasizes both the human and the computer side. Part I gives a thorough introduction to Bayesian networks as well as decision trees and infulence diagrams, and through examples and exercises, the reader is instructed in building graphical models from domain knowledge. This part is self-contained and it does not require other background than standard secondary school mathematics. Part II is devoted to the presentation of algorithms and complexity issues. This part is also self-contained, but it requires that the reader is familiar with working with texts in the mathematical language. The author also: *Provides a well-founded practical introduction to Bayesian networks, decision trees and influence diagrams *Gives several examples and exercises exploiting the computer systems for Bayesian netowrks and influence diagrams *Gives practical advice on constructiong Bayesian networks and influence diagrams from domain knowledge. *Embeds decision making into the framework of Bayesian networks *Presents in detail the currently most efficient algorithms for probability updating in Bayesian networks *Discusses a wide range of analyes tools and model requests together with algorithms for calculation of responses. *Gives a detailed presentation of the currently most efficient algorithm for solving influence diagrams.
Finn V. Jensen is professor of computer science at the University of Aalborg.
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About the Author
Finn V. Jensen is professor of computer science at the University of Aalborg.
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Bayesian Networks and Decision Graphs (Information Science and Statistics)
Seller: Maxwell's House of Books, La Mesa, CA, U.S.A.
Seller rating 3 out of 5 stars
Hardcover. Condition: Very Good. Dust Jacket Condition: No DJ as Issued. A lovely, crisp, clean hardcover in very good condition; light sunning to spine, light shelf wear. No dust jacket, as issued. We are a brick-and-mortar store and sell our own inventory. Seller Inventory # 063854
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Bayesian Networks and Decision Graphs
Published by
Springer-Verlag, NY, 2001
ISBN 10: 0387952594
ISBN 13: 9780387952598
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Hardcover
First Edition
Seller: Florida Mountain Book Co., Datil, NM, U.S.A.
Seller rating 5 out of 5 stars
Condition: Very Good. Hardcover, [xv], 268 pages. Very Good condition. First printing. Size 9.5"x6.25". "The book emphasizes both the human and the computer side. Part I gives a thorough introduction to Bayesian networks as well as decision trees and infulence diagrams, and through examples and exercises, the reader is instructed in building graphical models from domain knowledge. This part is self-contained and it does not require other background than standard secondary school mathematics. Part II is devoted to the presentation of algorithms and complexity issues. This part is also self-contained, but it requires that the reader is familiar with working with texts in the mathematical language." Book has light handling/shelfwear, spine is sunned, else Fine condition, clean and unmarked. Seller Inventory # 009774
Quantity: 1 available
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Bayesian Networks and Decision Graphs
Seller: Florida Mountain Book Co., Datil, NM, U.S.A.
Seller rating 5 out of 5 stars
Condition: Near Fine. Hardcover, [xv], 268 pages. Near Fine condition. Second, corrected printing, 2002. Size 9.5"x6.25". "The book emphasizes both the human and the computer side. Part I gives a thorough introduction to Bayesian networks as well as decision trees and infulence diagrams, and through examples and exercises, the reader is instructed in building graphical models from domain knowledge. This part is self-contained and it does not require other background than standard secondary school mathematics. Part II is devoted to the presentation of algorithms and complexity issues. This part is also self-contained, but it requires that the reader is familiar with working with texts in the mathematical language." Book has light exterior shelfwear, else Fine condition, clean and unmarked. Seller Inventory # 009576
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Bayesian networks and decision graphs Finn V. Jensen. Statistics for engineering and information science.
Published by
New York , Springer [2001]., 2001
ISBN 10: 0387952594
ISBN 13: 9780387952598
Used
Hardcover
Seller: Antiquariat Bookfarm, Löbnitz, Germany
Seller rating 5 out of 5 stars
Hardcover. Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de bibliothèque avec signature et cachet. BON état, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. 62 JEN 9780387952598 Sprache: Englisch Gewicht in Gramm: 550. Seller Inventory # 2498618
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Bayesian networks and decision graphs Finn V. Jensen. Statistics for engineering and information science.
Published by
New York , Springer [2001]., 2001
ISBN 10: 0387952594
ISBN 13: 9780387952598
Used
Hardcover
Seller: Antiquariat Bookfarm, Löbnitz, Germany
Seller rating 5 out of 5 stars
Hardcover. Ex-library with stamp and library-signature. GOOD condition, some traces of use. Ancien Exemplaire de bibliothèque avec signature et cachet. BON état, quelques traces d'usure. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. 62 JEN 9780387952598 Sprache: Englisch Gewicht in Gramm: 550. Seller Inventory # 2498631
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Bayesian Networks and Decision Graphs (Information Science and Statistics)
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Bayesian Networks and Decision Graphs (Information Science and Statistics)
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Bayesian Networks and Decision Graphs (Information Science and Statistics)
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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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