Probabilistic Graphical Models Principles and Applications

Probabilistic Graphical Models Principles and Applications / by Luis Enrique Sucar.

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 applicat...

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Bibliographic Details
Main Author: Sucar, Luis Enrique (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: London : Springer London : Imprint: Springer, 2015.
Edition:1st ed. 2015.
Series:Advances in Computer Vision and Pattern Recognition,
Springer eBook Collection.
Subjects:
Mathematical statistics.
Artificial intelligence.
Pattern recognition.
Probabilities.
Electrical engineering.
Electronic resources (E-books)
Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
Table of Contents:
  • 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.

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