Probabilistic Graphical Models, Softcover reprint of the original 1st ed. 2015 Principles and Applications Advances in Computer Vision and Pattern Recognition Series

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.

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