Bayesian Networks and Decision Graphs February 8, 2007 /

Bayesian Networks and Decision Graphs February 8, 2007 /

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
Main Authors: Jensen, Finn V. (Author), Nielsen, Thomas D. (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:English
Published: New York, NY : Springer New York, 2007.
Edition:Second Edition.
Series:Information Science and Statistics,
Subjects:
Life sciences.
Mathematical statistics.
Artificial intelligence.
Plant science.
Botany.
Probabilities.
Statistics.
Life Sciences.
Plant Sciences.
Probability Theory and Stochastic Processes.
Probability and Statistics in Computer Science.
Artificial Intelligence (incl. Robotics).
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
Online Access:Full Text via HEAL-Link
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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