Overview
- Includes exercises, suggestions for research projects, and example applications throughout the book
- Presents the main classes of PGMs under a single, unified framework
- Covers both the fundamental aspects and some of the latest developments in the field
- Includes supplementary material: sn.pub/extras
Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)
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About this book
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 principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Features: presents a unified framework encompassing all of the main classes of PGMs; describes the practical application of the different techniques; examines the latest developments in the field, covering multidimensional Bayesian classifiers, relational graphical models and causal models; provides exercises, suggestions for further reading, and ideas for research or programming projects at the end of each chapter.
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Table of contents (13 chapters)
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Front Matter
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Fundamentals
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Front Matter
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Decision Models
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Front Matter
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Relational and Causal Models
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Front Matter
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Back Matter
Authors and Affiliations
Accessibility Information
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Bibliographic Information
Book Title: Probabilistic Graphical Models
Book Subtitle: Principles and Applications
Authors: Luis Enrique Sucar
Series Title: Advances in Computer Vision and Pattern Recognition
DOI: https://doi.org/10.1007/978-1-4471-6699-3
Publisher: Springer London
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer-Verlag London 2015
Softcover ISBN: 978-1-4471-7054-9Published: 09 October 2016
eBook ISBN: 978-1-4471-6699-3Published: 19 June 2015
Series ISSN: 2191-6586
Series E-ISSN: 2191-6594
Edition Number: 1
Number of Pages: XXIV, 253
Number of Illustrations: 113 b/w illustrations, 4 illustrations in colour
Topics: Probability and Statistics in Computer Science, Artificial Intelligence, Pattern Recognition, Probability Theory and Stochastic Processes, Electrical Engineering