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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.

Probabilistic Graphical Models: Principles and Applications

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