Probabilistic Graphical Models Principles and Applications
This accessible text/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective.The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning
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Luis Enrique Sucar
Probabilistic Graphical Models Principles and Applications
Advances in Computer Vision and Pattern Recognition Founding editor Sameer Singh, Rail Vision, Castle Donington, UK Series editor Sing Bing Kang, Microsoft Research, Redmond, WA, USA Advisory Board Horst Bischof, Graz University of Technology, Austria Richard Bowden, University of Surrey, Guildford, UK Sven Dickinson, University of Toronto, ON, Canada Jiaya Jia, The Chinese University of Hong Kong, Hong Kong Kyoung Mu Lee, Seoul National University, South Korea Yoichi Sato, The University of Tokyo, Japan Bernt Schiele, Max Planck Institute for Computer Science, Saarbrücken, Germany Stan Sclaroff, Boston University, MA, USA
More information about this series at http://www.springer.com/series/4205
Luis Enrique Sucar
Probabilistic Graphical Models Principles and Applications
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Luis Enrique Sucar Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE) Santa María Tonantzintla Puebla Mexico
ISSN 2191-6586 ISSN 2191-6594 (electronic) Advances in Computer Vision and Pattern Recognition ISBN 978-1-4471-6698-6 ISBN 978-1-4471-6699-3 (eBook) DOI 10.1007/978-1-4471-6699-3 Library of Congress Control Number: 2015939664 Springer London Heidelberg New York Dordrecht © Springer-Verlag London 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer-Verlag London Ltd. is part of Springer Science+Business Media (www.springer.com)
To my family, Doris, Edgar and Diana, for their unconditional love and support
Foreword
Probabilistic graphical models (PGMs), and their use for reasoning intelligently under uncertainty, emerged in the 1980s within the statistical and artificial intelligence reasoning communities. The Uncertainty in Artificial Intelligence (UAI) conference became the premier forum for this blossoming research field. It was at UAI-92 in San Jose that I first met Enrique Sucar—both of us graduate students—where he presented his work on relational and tempor
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