Probabilistic Graphical Models

Principles and Applications

  • Luis Enrique Sucar

Part of the Advances in Computer Vision and Pattern Recognition book series (ACVPR)

Table of contents

  1. Front Matter
    Pages i-xxiv
  2. Fundamentals

    1. Front Matter
      Pages 1-1
    2. Luis Enrique Sucar
      Pages 3-13
    3. Luis Enrique Sucar
      Pages 15-26
    4. Luis Enrique Sucar
      Pages 27-38
  3. Probabilistic Models

    1. Front Matter
      Pages 39-39
    2. Luis Enrique Sucar
      Pages 41-62
    3. Luis Enrique Sucar
      Pages 63-82
    4. Luis Enrique Sucar
      Pages 83-99
    5. Luis Enrique Sucar
      Pages 101-136
    6. Luis Enrique Sucar
      Pages 137-159
    7. Luis Enrique Sucar
      Pages 161-177
  4. Decision Models

    1. Front Matter
      Pages 179-179
    2. Luis Enrique Sucar
      Pages 181-198
    3. Luis Enrique Sucar
      Pages 199-216
  5. Relational and Causal Models

    1. Front Matter
      Pages 217-217
    2. Luis Enrique Sucar
      Pages 219-235
    3. Luis Enrique Sucar
      Pages 237-246
  6. Back Matter
    Pages 247-253

About this book


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.


Bayesian Classifiers Bayesian Networks Decision Networks Hidden Markov Models Influence Diagrams Learning Graphical Models Markov Decision Processes Markov Random Fields Probabilistic Graphical Models Probabilistic Inference

Authors and affiliations

  • Luis Enrique Sucar
    • 1
  1. 1.Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)Santa María TonantzintlaMexico

Bibliographic information