Bayesian Networks and Decision Graphs

Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algor...

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Bibliographic Details
Call Number:Libro Electrónico
Main Authors: Nielsen, Thomas Dyhre (Author), VERNER JENSEN, FINN (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:Inglés
Published: New York, NY : Springer New York : Imprint: Springer, 2007.
Edition:2nd ed. 2007.
Series:Information Science and Statistics,
Subjects:
Botany.
Probabilities.
Computer science-Mathematics.
Mathematical statistics.
Artificial intelligence.
Statistics .
Plant Science.
Probability Theory.
Probability and Statistics in Computer Science.
Artificial Intelligence.
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Online Access:Texto Completo
Table of Contents:
  • Prerequisites on Probability Theory
  • Prerequisites on Probability Theory
  • Probabilistic Graphical Models
  • Causal and Bayesian Networks
  • Building Models
  • Belief Updating in Bayesian Networks
  • Analysis Tools for Bayesian Networks
  • Parameter estimation
  • Learning the Structure of Bayesian Networks
  • Bayesian Networks as Classifiers
  • Decision Graphs
  • Graphical Languages for Specification of Decision Problems
  • Solution Methods for Decision Graphs
  • Methods for Analyzing Decision Problems.

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