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.
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.
Probabilistic Graphical Models: Principles and Applications
355Probabilistic Graphical Models: Principles and Applications
355Hardcover(2nd ed. 2021)
Product Details
ISBN-13: | 9783030619428 |
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Publisher: | Springer International Publishing |
Publication date: | 12/23/2020 |
Series: | Advances in Computer Vision and Pattern Recognition |
Edition description: | 2nd ed. 2021 |
Pages: | 355 |
Product dimensions: | 6.10(w) x 9.25(h) x (d) |