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.

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
• 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
• Suggests possible course outlines for instructors in the preface 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.

Inhalt

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