Bayesian Networks and Decision Graphs by Finn V Jensen
Gives an introduction to Bayesian networks as well as decision trees and influence diagrams. This book embeds decision making into the framework of Bayesian networks and discusses a range of analyses tools and model requests together with algorithms for calculation of responses.-
Pattern Recognition and Machine Learning
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Novelty, Information and Surprise
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Computational Methods in Biometric Authentication
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Statistical Image Processing and Multidimensional Modeling
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The Practice of Time Series Analysis
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Computer Intrusion Detection and Network Monitoring
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Probabilistic Networks and Expert Systems
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Information Theoretic Learning
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Estimation of Dependences Based on Empirical Data
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Probabilistic Conditional Independence Structures
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Information and Complexity in Statistical Modeling
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Feedforward Neural Network Methodology
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Statistical and Inductive Inference by Minimum Message Length
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The Cross-Entropy Method
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Cumulative Sum Charts and Charting for Quality Improvement
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Nonlinear Dimensionality Reduction
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Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis
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Support Vector Machines
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The Nature of Statistical Learning Theory
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Sequential Monte Carlo Methods in Practice
MATHEMATICAL REVIEWS
"This is indeed an invaluable text for students in information technology, engineering, and statisticsIt 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 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)
| SKU | Unavailable |
| ISBN 13 | 9780387952598 |
| ISBN 10 | 0387952594 |
| Title | Bayesian Networks and Decision Graphs |
| Author | Finn V Jensen |
| Series | Information Science And Statistics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Springer-Verlag New York Inc. |
| Year published | 2002-05-31 |
| Number of pages | 283 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |