Bayesian Networks and Decision Graphs (Information Science and...

Features

• Cover Type: Hard Cover with 448 pages
• Published by: Springer; 2nd ed. edition June 6, 2007
• Written in: English
• ISBN 10 Number: 0387682813
• ISBN 13 Number: 978-0387682815
• Book Dimensions: 9.3 x 6.3 x 1.1 inches
• Weighs: 1.6 pounds

Product 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-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 requirements a reference for a course on probabilistic reasoning in AI." (Luigi Portinale, The Computer Journal, Vol. 46 (3), 2003)

"This book is an introduction to Bayesian networks at an accessible level for first-year graduate or advanced undergraduate students.  I found this book to be an great 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)

"I can comfortably recommend this book as a primary source for topics related to Bayesian networks and decision graphs. This would be an great edition to any personal library." (Technometrics, Feburary 2008)

Product Description


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.

Reader Reviews
This review is from: Bayesian Networks and Decision Graphs (Information Science and Statistics) (Hardcover) Among currently available introduction to Bayesian networks (also known as Bayes Net, Bayesian Belief Nets), this book is probably one of the most accessible. The book is divided into part I and II. Part I is intended for BN users (practitioners) and Part II more towards BN developers and researchers, as it contains algorithmic introduction of BN. Prerequisites of the book as stated in the preface include Graph Theory and Calculus, both at introductory level. I personally did not have exposure to Graph theory, but I was able to understand most of the material without any help. Necessary probability theory is developed, but basic probability knowledge is also a prerequisite to digest the material to a reader without prior exposure of Probability as it shapes the core of the material in the book. The strength of this text is in Part I where the author provides several examples to illustrate use of Bayesian Networks, Influence Diagrams and other models. I find it useful Influence Diagram as an extension of Bayesian Networks. Most answers to Exercises at the end of each chapter are provided at the author's homepage, except answers of the last chapter. Answers that require graphical modeling software are also provided in Hugin format. (Hugin Lite can be downloaded from Hugin site.) The downsides are that writing of the text is somewhat awkward, obscuring readers from understanding, that model building chapter could have been discussed more thoroughly, that material in Learning is barely present, and that definitions are sometimes not introduced upon the first encounter but they appear later in chapters. More different and complex examples could have been discussed to illustrate the material. Note: the author provides a page for Learning at his homepage. Although this is an introduction to Bayesian Networks and Influence Diagrams, a reader should be equipped with some level of abstract thinking in order to digest the material. This book is suitable for self-study. It has motivations for the uninitiated. References are provided at the end of the book and I was able to find some of them online. A notable is "A tutorial on Learning with Bayesian Networks" by Heckerman, to fill in the part of Learning in this book. Other books at this level from users' perspective are: Edwards, Introduction to Graphical Modeling (Utilizes software MIM.) Clemen, et al., Making Hard Decisions (Uses Palisade Decision Tools suite. The book discusses Influence Diagrams but not Bayesian Networks.) Further studies after completion of this book include: Cowell, et al., Probabilistic Networks and Expert Systems Lauritzen, Graphical Models Pearl, Probabilistic Reasoning in Intelligent Systems Pearl, Causality