Bayesian Networks and Decision Graphs (Information Science and Statistics) - Hardcover

Jensen, Finn V.; Nielsen, Thomas

Hardcover

ISBN 10: 0387952594 ISBN 13: 9780387952598

Publisher: Springer-Verlag New York Inc., 2002

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 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.

About this title

Review

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)

From the Back Cover

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.

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• 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.

Finn V. Jensen is a professor at the department of computer science at Aalborg University, Denmark.

Thomas D. Nielsen is an associate professor at the same department.

Edition details

• Publisher: Springer-Verlag New York Inc.
• Publication date: 2002
• ISBN 10: 0387952594
• ISBN 13: 9780387952598
• Binding: Hardcover
• Language: English
• Edition number: 2
• Number of pages: 283