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Probabilistic Networks

  • Uffe B. Kjærulff
  • Anders L. Madsen
Part of the Information Science and Statistics book series (ISS, volume 22)

Abstract

Probabilistic networks are graphical models of (causal) interactions among a set of variables, where the variables are represented as vertices (nodes) of a graph and the interactions (direct dependences) as directed edges (links or arcs) between the vertices. Any pair of unconnected vertices of such a graph indicates (conditional) independence between the variables represented by these vertices under particular circumstances that can easily be read from the graph. Hence, probabilistic networks capture a set of (conditional) dependence and independence properties associated with the variables represented in the network.

Keywords

Utility Function Bayesian Network Joint Probability Distribution Dynamic Bayesian Network Discrete Random Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Uffe B. Kjærulff
    • 1
  • Anders L. Madsen
    • 1
    • 2
  1. 1.Department of Computer ScienceAalborg UniversityAalborgDenmark
  2. 2.HUGIN EXPERT A/SAalborgDenmark

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