Authors:
- 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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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
About this book
Authors and Affiliations
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Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Santa María Tonantzintla, Mexico
Luis Enrique Sucar
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