Probabilistic Graphical Models: Principles and Applications

✍ Scribed by Luis Enrique Sucar

Publisher: Springer
Year: 2015
Tongue: English
Leaves: 267
Series: Advances in Computer Vision and Pattern Recognition
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

✦ Table of Contents

Front Matter....Pages i-xxiv
Front Matter....Pages 1-1
Introduction....Pages 3-13
Probability Theory....Pages 15-26
Graph Theory....Pages 27-38
Front Matter....Pages 39-39
Bayesian Classifiers....Pages 41-62
Hidden Markov Models....Pages 63-82
Markov Random Fields....Pages 83-99
Bayesian Networks: Representation and Inference....Pages 101-136
Bayesian Networks: Learning....Pages 137-159
Dynamic and Temporal Bayesian Networks....Pages 161-177
Front Matter....Pages 179-179
Decision Graphs....Pages 181-198
Markov Decision Processes....Pages 199-216
Front Matter....Pages 217-217
Relational Probabilistic Graphical Models....Pages 219-235
Graphical Causal Models....Pages 237-246
Back Matter....Pages 247-253

✦ Subjects

Machine Learning; Probabilistic Models; Bayesian Networks; Decision Trees; Markov Networks; Bayesian Inference; Classification; Naive Bayes; Graph Theory; Probability Theory; Gesture Recognition; Causality; Statistical Inference; Mathematical Logic; Uncertainty; Markov Models; PageRank; Markov Decision Process

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