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.
Cuprins
Part I: Fundamentals Introduction Probability Theory Graph Theory Part II: Probabilistic Models Bayesian Classifiers Hidden Markov Models Markov Random Fields Bayesian Networks: Representation and Inference Bayesian Networks: Learning Dynamic and Temporal Bayesian Networks Part III: Decision Models Decision Graphs Markov Decision Processes Part IV: Relational and Causal Models Relational Probabilistic Graphical Models Graphical Causal Models
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