Dawn E. Holmes and Lakhmi C. Jain (Eds.) InnovationsinBayesianNetworks Studies in Computational Intelligence,Volume 156 Editor-in-Chief Prof. Janusz Kacprzyk Systems Research Institute Polish Academy of Sciences ul. Newelska 6 01-447 Warsaw Poland E-mail: [email protected] Further volumes of this series can be found on our homepage: Vol. 145. Mikhail Ju. Moshkov, Marcin Piliszczuk springer.com and Beata Zielosko Partial Covers, Reducts and Decision Rules in Rough Sets, 2008 Vol. 134. Ngoc Thanh Nguyen and Radoslaw Katarzyniak (Eds.) ISBN 978-3-540-69027-6 New Challenges in Applied Intelligence Technologies, 2008 Vol. 146. Fatos Xhafa and Ajith Abraham (Eds.) ISBN 978-3-540-79354-0 Metaheuristics for Scheduling in Distributed Computing Vol. 135. Hsinchun Chen and Christopher C.Yang (Eds.) Environments, 2008 Intelligence and Security Informatics, 2008 ISBN 978-3-540-69260-7 ISBN 978-3-540-69207-2 Vol. 147. Oliver Kramer Self-Adaptive Heuristics for Evolutionary Computation, 2008 Vol. 136. Carlos Cotta, Marc Sevaux ISBN 978-3-540-69280-5 and Kenneth S¨orensen (Eds.) Adaptive and Multilevel Metaheuristics, 2008 Vol. 148. Philipp Limbourg ISBN 978-3-540-79437-0 Dependability Modelling under Uncertainty, 2008 ISBN 978-3-540-69286-7 Vol. 137. Lakhmi C. Jain, Mika Sato-Ilic, Maria Virvou, George A. Tsihrintzis,Valentina Emilia Balas Vol. 149. Roger Lee (Ed.) and Canicious Abeynayake (Eds.) Software Engineering, Artificial Intelligence, Networking and Computational Intelligence Paradigms, 2008 Parallel/Distributed Computing, 2008 ISBN 978-3-540-79473-8 ISBN 978-3-540-70559-8 Vol. 138. Bruno Apolloni,Witold Pedrycz, Simone Bassis Vol. 150. Roger Lee (Ed.) and Dario Malchiodi Software Engineering Research, Management and The Puzzle of Granular Computing, 2008 Applications, 2008 ISBN 978-3-540-79863-7 ISBN 978-3-540-70774-5 Vol. 139. Jan Drugowitsch Vol. 151. Tomasz G. Smolinski, Mariofanna G. Milanova Design and Analysis of Learning Classifier Systems, 2008 and Aboul-Ella Hassanien (Eds.) ISBN 978-3-540-79865-1 Computational Intelligence in Biomedicine and Bioinformatics, 2008 Vol. 140. Nadia Magnenat-Thalmann, Lakhmi C. Jain ISBN 978-3-540-70776-9 and N. Ichalkaranje (Eds.) New Advances in Virtual Humans, 2008 Vol. 152. Jaroslaw Stepaniuk ISBN 978-3-540-79867-5 Rough – Granular Computing in Knowledge Discovery and Data Mining, 2008 Vol. 141. Christa Sommerer, Lakhmi C. Jain ISBN 978-3-540-70800-1 and Laurent Mignonneau (Eds.) The Art and Science of Interface and Interaction Design (Vol. 1), Vol. 153. Carlos Cotta and Jano van Hemert (Eds.) 2008 Recent Advances in Evolutionary Computation for ISBN 978-3-540-79869-9 Combinatorial Optimization, 2008 ISBN 978-3-540-70806-3 Vol. 142. George A. Tsihrintzis, Maria Virvou, Robert J. Howlett and Lakhmi C. Jain (Eds.) Vol. 154. Oscar Castillo, Patricia Melin, Janusz Kacprzyk and New Directions in Intelligent Interactive Multimedia, 2008 Witold Pedrycz (Eds.) ISBN 978-3-540-68126-7 Soft Computing for Hybrid Intelligent Systems, 2008 ISBN 978-3-540-70811-7 Vol. 143. Uday K. Chakraborty (Ed.) Advances in Differential Evolution, 2008 Vol. 155. Hamid R. Tizhoosh and M.Ventresca (Eds.) ISBN 978-3-540-68827-3 Oppositional Concepts in Computational Intelligence, 2008 ISBN 978-3-540-70826-1 Vol. 144.Andreas Fink and Franz Rothlauf (Eds.) Advances in Computational Intelligence in Transport, Logistics, Vol. 156. Dawn E. Holmes and Lakhmi C. Jain (Eds.) and Supply Chain Management, 2008 Innovations in Bayesian Networks, 2008 ISBN 978-3-540-69024-5 ISBN 978-3-540-85065-6 Dawn E. Holmes Lakhmi C. Jain (Eds.) Innovations in Bayesian Networks Theory and Applications 123 Prof.DawnE.Holmes Department of Statistics and Applied Probability University of California Santa Barbara, CA 93106 USA Email: [email protected] Prof.LakhmiC.Jain Professor of Knowledge-Based Engineering University of South Australia Adelaide Mawson Lakes, SA 5095 Australia Email: [email protected] ISBN 978-3-540-85065-6 e-ISBN 978-3-540-85066-3 DOI 10.1007/978-3-540-85066-3 Studies in Computational Intelligence ISSN 1860949X Library of Congress Control Number: 2008931424 c 2008 Springer-Verlag Berlin Heidelberg This work is subject to copyright. 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Printed in acid-free paper 987654321 springer.com For: COLIN and HELEN Preface There are many invaluable books available on Bayesian networks. However, in com- piling a volume titled “Innovations in Bayesian networks” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field. So, what are Bayesian networks? Bayesian networks utilize the probability calculus together with an underlying graphical structure to provide a theoretical framework for modeling uncertainty. Although the philosophical roots of the subject may be traced back to Bayes and the foundations of probability, Bayesian networks as such are a modern device, first appearing in Pearl (1988), and growing out of the research in expert or intelligent systems. In Pearl’s ground-breaking Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, (1988), he presents Bayesian networks for the first time. Since then, Bayesian networks have become the vehicle of choice in many AI applications. In compiling this volume we have sought to present innovative research from pres- tigious contributors in the field of Bayesian networks. Each chapter is self-contained and is described below. Chapter 1 by Holmes presents a brief introduction to Bayesian networks for readers entirely new to the field. Chapter 2 by Neapolitan, a self-proclaimed convert to Bayesianism, discusses the modern revival of Bayesian statistics research, due in particular to the advent of Bayesian networks. Bayesian and frequentist approaches are compared, with an em- phasis on interval estimation and hypothesis testing. For Chapter 3 we are indebted to MIT Press and Kluwer books for permission to reprint Heckerman’s famous tutorial on learning with Bayesian networks, in which the reader is introduced to many of the techniques fundamental to the success of this formalism. In Chapter 4, Korb and Nicholson discuss some of the philosophical problems as- sociated with Bayesian networks. In particular, they introduce a causal interpretation of Bayesian networks thus providing a valuable addition to the currently lively and productive research in causal modeling. Chapter 5 by Niedermayer explores how Bayesian Network theory affects issues of advanced computability. Some interesting applications together with a brief discus- sion of the appropriateness and limitations of Bayesian Networks for human-computer interaction and automated learning make this a strong contribution to the field. VIII Preface In Chapter 6 Nagl, Williams and Williamson introduce and discuss Objective Bayesian nets and their role in knowledge integration. Their importance for medical informatics is emphasized and a scheme for systems modeling and prognosis in breast cancer is presented. Chapter 7 by Jiang, Wagner, and Cooper considers epidemic modeling. Epidemic curves are defined and a Bayesian network model for real-time estimation of these curves is give. An evaluation of the experimental results and their accuracy rounds off this valuable contribution. Chapter 8 by Lauría begins with an excellent introduction to structure learning in Bayesian Networks. The paper continues by exploring the feasibility of applying an information-geometric approach to the task of learning the topology of Bayesian net- work and also provides an introduction to information geometry. Chapter 9 by Maes, Leray and Meganck discusses causal graphical models for dis- crete variables that can handle latent variables without explicitly modeling them quan- titatively. The techniques introduced in this chapter have been partially implementing into the structure learning package (SLP) of the Bayesian networks toolbox (BNT) for MATLAB. Chapter 10 by Flores, Gamez, and Moral presents a new approach to the problem of obtaining the most probable explanations given a set of observations in a Bayesian network. Examples are given and a set of experiments to make a comparison with other existing abductive techniques that were designed with goals similar to those we pursue is described. Chapter 11 by Holmes describes a maximum entropy approach to inference in Bayesian networks. We are indebted to the American Institute of Physics for kind permission to reproduce this paper. Chapter 12 by de Salvo Braz, Amir, and Roth presents a survey of first-order prob- abilistic models. The decision on using directed or undirected models is discussed, as are infinite models. The authors conclude that such algorithms would benefit from choosing to process first the parts of the model that yield greater amounts of informa- tion about the query. This book will prove valuable to theoreticians as well as application scien- tists/engineers in the area of Bayesian networks. Postgraduate students will also find this a useful sourcebook since it shows the direction of current research. We have been fortunate in attracting top class researchers as contributors and wish to offer our thanks for their support in this project. We also acknowledge the expertise and time of the reviewers. Finally, we also wish to thank Springer for their support.