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
- Fully updated new edition, featuring a greater number of exercises, and new material on partially observable Markov decision processes, and graphical models and deep learning
Part of the book series: Advances in Computer Vision and Pattern Recognition (ACVPR)
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Table of contents (16 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, Causal and Deep Models
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Front Matter
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Keywords
About this book
This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python.
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.Topics and features:
- Presents a unified framework encompassing all of the main classes of PGMs
- Explores the fundamental aspects of representation, inference and learning for each technique
- Examines new material on partially observable Markov decision processes, and graphical models
- Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models
- Covers multidimensional Bayesian classifiers, relational graphical models, and causal models
- Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects
- Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks
- Outlines the practical application of the different techniques
- Suggests possible course outlines for instructors
This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference.
Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.
Authors and Affiliations
About the author
Dr. Luis Enrique Sucar is a Senior Research Scientist in the Department of Computing at the National Institute of Astrophysics, Optics and Electronics (INAOE), Mexico.
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/10.1007/978-3-030-61943-5
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-61942-8Published: 24 December 2020
Softcover ISBN: 978-3-030-61945-9Published: 24 December 2021
eBook ISBN: 978-3-030-61943-5Published: 23 December 2020
Series ISSN: 2191-6586
Series E-ISSN: 2191-6594
Edition Number: 2
Number of Pages: XXVIII, 355
Number of Illustrations: 23 b/w illustrations, 144 illustrations in colour
Topics: Probability and Statistics in Computer Science, Artificial Intelligence, Pattern Recognition, Probability Theory and Stochastic Processes, Electrical Engineering