Overview
- Gives a well-founded practical introduction to Bayesian networks
- Includes presentation of the most efficient algorithm for solving influence diagrams
Part of the book series: Information Science and Statistics (ISS)
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Table of contents (7 chapters)
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Front Matter
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A Practical Guide to Normative Systems
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Front Matter
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Algorithms for Normative Systems
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Front Matter
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Back Matter
Keywords
About this book
- 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.
Reviews
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 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 excellent introduction to the topic. It is well written, provides broad topic coverage, and is quiteaccessible 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 excellent edition to any personal library." (Technometrics, Feburary 2008)
From the reviews of the second edition:
"The present book provides a very readable but also rigorous and comprehensive introduction to the subject. It would make a very good text for a graduate or an advanced undergraduate course. … Altogether, this is a very useful book for anyone interested in learning Bayesian networks without tears." (Jayanta K. Ghosh, International Statistical Reviews, Vol. 76 (2), 2008)
"This book is the second edition of Jensen’s Bayesian Networks and Decision Graphs … . Each chapter ends with a summary section, bibliographic notes, and exercises. … provides a readable, self-contained, and above all, practical introduction to Bayesian networks and decision graphs. Its treatment is appropriate not just for statisticians, but also for computer scientists, engineers, and others researchers with appropriate mathematical background. … highly recommend it as a text or a useful reference for anyone interested in probabilistic graphical models or decision graphs." (Alyson G. Wilson, Journal of the American Statistical Association, Vol. 104 (485), March, 2009)
Authors and Affiliations
Bibliographic Information
Book Title: Bayesian Networks and Decision Graphs
Authors: Finn V. Jensen
Series Title: Information Science and Statistics
DOI: https://doi.org/10.1007/978-1-4757-3502-4
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 2001
eBook ISBN: 978-1-4757-3502-4Published: 29 June 2013
Series ISSN: 1613-9011
Series E-ISSN: 2197-4128
Edition Number: 1
Number of Pages: XV, 268
Number of Illustrations: 4 b/w illustrations
Topics: Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences, Artificial Intelligence, Probability and Statistics in Computer Science