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Sinopsis
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 sides. Part I gives a thorough introduction to Bayesian networks as well as decision trees and influence 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 networks and influence diagrams; gives practical advice on constructing 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 analyses tools and model requests together with algorithms for calculation of responses; and gives a detailed presentation of the currently most efficient algorithm for solving influence diagrams.
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Críticas
From the reviews: MATHEMATICAL REVIEWS "This is indeed an invaluable text for students in information technology, engineering, and statistics. It is also very helpful for researchers in these fields and for those working in industry. The book is self-contained???The book has enough illustrative examples and exercises for the reader. All the illustrations are motivated by real applications. Moreover, the book provides a good balance between pure mathematical treatment and the applied aspects of the subject." "The Bayesian network (BN), or probabilistic expert system, is technology for automating human-life reasoning under uncertainty in specific contexts. ??? the book does an admirable job of concisely explaining a great range of concepts and techniques. ??? the book is very well written and to my knowledge nothing else meets its specific goal of quickly equipping the reader with both practical skills and sufficient theoretical background. ??? I certainly would not want to try to implement a BN application without reading this book.??? (David Tritchler, Sankhya: Indian Journal of Statistics, Vol. 64 (B Part 3), 2002) "Professor Jensen is certainly one of the most influential researchers in the field of Bayesian networks and it is not surprising that this book represents a very clear and useful presentation of the main properties and use of graphical models. ??? I think that the present volume represents a useful integration of other material and a compact guide for either a student who wants an introduction to the field or a teacher who needs a reference for a course on probabilistic reasoning in AI." (Luigi Portinale, The Computer Journal, Vol. 46 (3), 2003) "This bookis an introduction to Bayesian networks at an accessible level for first-year graduate or advanced undergraduate students. ??? I found this book to be an excellent introduction to the topic. It is well written, provides broad topic coverage, and is quite accessible to the non-expert. ??? I think Bayesian Networks and Decision Graphs would make a fine text for an introductory class in Bayesian networks or a useful reference for anyone interested in learning about the field." (David J. Marchette, Technometrics, Vol. 45 (2), 2003) From the reviews: MATHEMATICAL REVIEWS "This is indeed an invaluable text for students in information technology, engineering, and statistics. It is also very helpful for researchers in these fields and for those working in industry. The book is self-containedThe book has enough illustrative examples and exercises for the reader. All the illustrations are motivated by real applications. Moreover, the book provides a good balance between pure mathematical treatment and the applied aspects of the subject." "The Bayesian network (BN), or probabilistic expert system, is technology for automating human-life reasoning under uncertainty in specific contexts. the book does an admirable job of concisely explaining a great range of concepts and techniques. the book is very well written and to my knowledge nothing else meets its specific goal of quickly equipping the reader with both practical skills and sufficient theoretical background. I certainly would not want to try to implement a BN application without reading this book. (David Tritchler, Sankhya: Indian Journal of Statistics, Vol. 64 (B Part 3), 2002) "Professor Jensen is certainly one of the most influential researchers in the field of Bayesian networks and it is not surprising that this book represents a very clear and useful presentation of the main properties and use of graphical models. I think that the present volume represents a useful integration of other material and a compact guide for either a student who wants an introduction to the field or a teacher who needs a reference for a course on probabilistic reasoning in AI." (Luigi Portinale, The Computer Journal, Vol. 46 (3), 2003) "This book is an introduction toBayesian networks at an accessible level for first-year graduate or advanced undergraduate students. I found this book to be an excellent introduction to the topic. It is well written, provides broad topic coverage, and is quite accessible to the non-expert. I think Bayesian Networks and Decision Graphs would make a fine text for an introductory class in Bayesian networks or a useful reference for anyone interested in learning about the field." (David J. Marchette, Technometrics, Vol. 45 (2), 2003)
Reseña del editor
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 sides. Part I gives a thorough introduction to Bayesian networks as well as decision trees and influence 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 networks and influence diagrams; gives practical advice on constructing 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 analyses tools and model requests together with algorithms for calculation of responses; and gives a detailed presentation of the currently most efficient algorithm for solving influence diagrams.
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- EditorialSpringer-Verlag New York Inc.
- Año de publicación2002
- ISBN 10 0387952594
- ISBN 13 9780387952598
- EncuadernaciónTapa dura
- IdiomaInglés
- Número de páginas283
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Bayesian networks and decision graphs Finn V. Jensen. Statistics for engineering and information science.
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New York , Springer [2001]., 2001
ISBN 10: 0387952594
ISBN 13: 9780387952598
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Librería: Antiquariat Bookfarm, Löbnitz, Alemania
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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. Nº de ref. del artículo: 2498618
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Bayesian networks and decision graphs Finn V. Jensen. Statistics for engineering and information science.
Publicado por
New York , Springer [2001]., 2001
ISBN 10: 0387952594
ISBN 13: 9780387952598
Antiguo o usado
Tapa dura
Librería: Antiquariat Bookfarm, Löbnitz, Alemania
Calificación del vendedor: 5 de 5 estrellas
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. Nº de ref. del artículo: 2498631
Cantidad disponible: 1 disponibles
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Bayesian Networks and Decision Graphs
Publicado por
Springer-Verlag, NY, 2001
ISBN 10: 0387952594
ISBN 13: 9780387952598
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Librería: Florida Mountain Book Co., Datil, NM, Estados Unidos de America
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Condición: 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. Nº de ref. del artículo: 009774
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Bayesian Networks and Decision Graphs
Publicado por
Springer-Verlag, NY, 2002
ISBN 10: 0387952594
ISBN 13: 9780387952598
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Tapa dura
Librería: Florida Mountain Book Co., Datil, NM, Estados Unidos de America
Calificación del vendedor: 5 de 5 estrellas
Condición: 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. Nº de ref. del artículo: 009576
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Bayesian Networks and Decision Graphs (Information Science and Statistics)
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Hardcover. Condición: Very Good. Estado de la sobrecubierta: 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. Nº de ref. del artículo: 063854
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Bayesian Networks and Decision Graphs
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Bayesian Networks and Decision Graphs. Corrected Printing (Information Science and Statistics)
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Condición: Very Good. Corrected Printing, 268 pp., Hardcover, minor internal library markings else text clean & binding tight. - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country. Nº de ref. del artículo: ZB1321285
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Bayesian Networks and Decision Graphs (Information Science and Statistics)
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ISBN 10: 0387952594
ISBN 13: 9780387952598
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Bayesian Networks and Decision Graphs
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Condición: New. pp. xv + 268 Illus. Nº de ref. del artículo: 5770412
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