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Probabilistic Graphical Models

Principles and Applications

  • Textbook
  • © 2015

Overview

Authors:
  1. Luis Enrique Sucar

    1. Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Santa María Tonantzintla, Mexico

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  • Includes exercises, suggestions for research projects, and example applications throughout the book
  • Presents the main classes of PGMs under a single, unified framework
  • Covers both the fundamental aspects and some of the latest developments in the field
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (13 chapters)

  1. Front Matter

    Pages i-xxiv
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  2. Fundamentals

    1. Front Matter

      Pages 1-1
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    2. Introduction

      • Luis Enrique Sucar
      Pages 3-13

    3. Probability Theory

      • Luis Enrique Sucar
      Pages 15-26

    4. Graph Theory

      • Luis Enrique Sucar
      Pages 27-38

  3. Probabilistic Models

    1. Front Matter

      Pages 39-39
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    2. Bayesian Classifiers

      • Luis Enrique Sucar
      Pages 41-62

    3. Hidden Markov Models

      • Luis Enrique Sucar
      Pages 63-82

    4. Markov Random Fields

      • Luis Enrique Sucar
      Pages 83-99

    5. Bayesian Networks: Representation and Inference

      • Luis Enrique Sucar
      Pages 101-136

    6. Bayesian Networks: Learning

      • Luis Enrique Sucar
      Pages 137-159

    7. Dynamic and Temporal Bayesian Networks

      • Luis Enrique Sucar
      Pages 161-177

  4. Decision Models

    1. Front Matter

      Pages 179-179
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    2. Decision Graphs

      • Luis Enrique Sucar
      Pages 181-198

    3. Markov Decision Processes

      • Luis Enrique Sucar
      Pages 199-216

  5. Relational and Causal Models

    1. Front Matter

      Pages 217-217
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    2. Relational Probabilistic Graphical Models

      • Luis Enrique Sucar
      Pages 219-235

    3. Graphical Causal Models

      • Luis Enrique Sucar
      Pages 237-246

  6. Back Matter

    Pages 247-253
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Keywords

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. 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.

Authors and Affiliations

  • Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE), Santa María Tonantzintla, Mexico

    Luis Enrique Sucar

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