Bayesian Networks and Influence Diagrams A Guide to Construction and
Probabilistic networks, also known as Bayesian networks and influence diagrams, have become one of the most promising technologies in the area of applied artificial intelligence, offering intuitive, efficient, and reliable methods for diagnosis, predictio
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Uffe B. Kjaerulff • Anders L. Madsen
Bayesian Networks and Influence Diagrams A Guide to Construction and Analysis
Information Science and Statistics Series Editors: M. Jordan J. Kleinberg B. Sch¨olkopf
Information Science and Statistics Akaike/Kitagawa: The Practice of Time Series Analysis Bishop: Pattern Recognition and Machine Learning Cowell/Dawid/Lauritzen/Spiegelhalter: Probabilistic Networks and Expert Systems Doucet/de Freitas/Gordon: Sequential Monte Carlo Methods in Practice Fine: Feedforward Neural Network Methodology Hawkins/Olwell: Cumulative Sum Charts and Charting for Quality Improvement Jensen/Nielsen: Bayesian Networks and Decision Graphs, Second Edition Kjærulff/Madsen: Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis Lee/Verleysen: Nonlinear Dimensionality Reduction Marchette: Computer Intrusion Detection and Network Monitoring: A Statistical Viewpoint Rissanen: Information and Complexity in Statistical Modeling Rubinstein/Kroese: The Cross-Entropy Method: A Unified Approach to Combinatorial Optimization, Monte Carlo Simulation, and Machine Learning Studen´y: Probabilistic Conditional Independence Structures Vapnik: The Nature of Statistical Learning Theory, Second Edition Wallace: Statistical and Inductive Inference by Minimum Massage Length
Uffe B. Kjærulff
•
Anders L. Madsen
Bayesian Networks and Influence Diagrams A Guide to Construction and Analysis
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Uffe B. Kjærulff Department of Computer Science Aalborg University Selma Lagerl¨ofs Vej 300 DK-9220 Aalborg Denmark [email protected]
Anders L. Madsen HUGIN Expert A/S Gasværksvej 5 DK-9000 Aalborg Denmark [email protected]
Series Editors Michael Jordan Division of Computer Science and Department of Statistics University of California, Berkeley Berkeley, CA 94720 USA
Jon Kleinberg Department of Computer Science Cornell University Ithaca, NY 14853 USA
Bernhard Sch¨olkopf Max Planck Institute for Biological Cybernetics Spemmannstrasse 38 72076 T¨ubingen Germany
ISBN: 978-0-387-74100-0 e-ISBN: 978-0-387-74101-7 DOI: 10.1007/978-0-387-74101-7 Library of Congress Control Number: 2007940536 c 2008 Springer Science+Business Media, LLC All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. Printed on acid-free paper 987654321 springer.com
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Preface
This book is a monograph on practical aspects of probabilistic networks (a
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