Graph Theory
In this chapter, a review of some aspects of graph theory that are important for probabilistic graphical models are presented. After providing a definition of directed and undirected graphs, some basic theoretical graph concepts are introduced, including
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Luis Enrique Sucar
Probabilistic Graphical Models Principles and Applications Second Edition
Advances in Computer Vision and Pattern Recognition Founding Editor Sameer Singh, Rail Vision, Castle Donington, UK Series Editor Sing Bing Kang, Zillow, Inc., Seattle, WA, USA Advisory Editors Horst Bischof, Graz University of Technology, Graz, Austria Richard Bowden, University of Surrey, Guildford, Surrey, UK Sven Dickinson, University of Toronto, Toronto, ON, Canada Jiaya Jia, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong Kyoung Mu Lee, Seoul National University, Seoul, Korea (Republic of) Yoichi Sato, University of Tokyo, Tokyo, Japan Bernt Schiele, Max Planck Institute for Informatics, Saarbrücken, Saarland, Germany Stan Sclaroff, Boston University, Boston, MA, USA
More information about this series at http://www.springer.com/series/4205
Luis Enrique Sucar
Probabilistic Graphical Models Principles and Applications Second Edition
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Luis Enrique Sucar Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE) San Andrés Cholula, Puebla, Mexico
ISSN 2191-6586 ISSN 2191-6594 (electronic) Advances in Computer Vision and Pattern Recognition ISBN 978-3-030-61942-8 ISBN 978-3-030-61943-5 (eBook) https://doi.org/10.1007/978-3-030-61943-5 1st edition: © Springer-Verlag London 2015 2nd edition: © Springer Nature Switzerland AG 2021 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, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
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
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