Probabilistic Graphical Models Principles and Applications /

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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Sucar, Luis Enrique (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Springer London : Imprint: Springer, 2015.
Edición:1st ed. 2015.
Colección:Advances in Computer Vision and Pattern Recognition,
Temas:
Computer science-Mathematics.
Mathematical statistics.
Artificial intelligence.
Pattern recognition systems.
Probabilities.
Electrical engineering.
Probability and Statistics in Computer Science.
Artificial Intelligence.
Automated Pattern Recognition.
Probability Theory.
Electrical and Electronic Engineering.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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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