Probabilistic Graphical Models Principles and Applications, Second Edition

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.
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