Advertisement

© 2021

Probabilistic Graphical Models

Principles and Applications

Textbook
  • 10k Downloads

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

Table of contents

  1. Front Matter
    Pages i-xxviii
  2. Fundamentals

    1. Front Matter
      Pages 1-1
    2. Luis Enrique Sucar
      Pages 3-14
    3. Luis Enrique Sucar
      Pages 15-26
    4. Luis Enrique Sucar
      Pages 27-39
  3. Probabilistic Models

    1. Front Matter
      Pages 41-41
    2. Luis Enrique Sucar
      Pages 43-69
    3. Luis Enrique Sucar
      Pages 71-91
    4. Luis Enrique Sucar
      Pages 93-110
    5. Luis Enrique Sucar
      Pages 111-151
    6. Luis Enrique Sucar
      Pages 153-179
    7. Luis Enrique Sucar
      Pages 181-202
  4. Decision Models

    1. Front Matter
      Pages 203-203
    2. Luis Enrique Sucar
      Pages 205-228
    3. Luis Enrique Sucar
      Pages 229-248
    4. Luis Enrique Sucar
      Pages 249-266
  5. Relational, Causal and Deep Models

    1. Front Matter
      Pages 267-267
    2. Luis Enrique Sucar
      Pages 269-286
    3. Luis Enrique Sucar
      Pages 287-305
    4. Luis Enrique Sucar
      Pages 307-325

About this book

Introduction

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective.  It features new material on partially observable Markov decision processes, graphical models, and deep learning, as well as an even greater number of exercises.

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
  • Examines new material on partially observable Markov decision processes, and graphical models
  • Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models 
  • Covers multidimensional Bayesian classifiers, relational graphical models, and causal models
  • Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects
  • Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks
  • Outlines the practical application of the different techniques
  • Suggests possible course outlines for instructors

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.

Keywords

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

  1. 1.Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE)San Andrés CholulaMexico

About the authors

Dr. Luis Enrique Sucar is a Senior Research Scientist in the Department of Computing at the National Institute of Astrophysics, Optics and Electronics (INAOE), Mexico.

Bibliographic information