Credal and Bayesian Networks

  • Rolf Haenni
  • Jan-Willem Romeijn
  • Gregory Wheeler
  • Jon Williamson
Part of the Synthese Library book series (SYLI, volume 350)


We have seen that in many cases it suffices to restrict attention to convex sets of probability functions, and even, in the case of objective Bayesianism, often a single probability function. This restriction will be important in what follows, since it will allow us to exploit the computational machinery of probabilistic networks—in particular credal networks and Bayesian networks —to help us answer the fundamental question.


Convex Hull Bayesian Network Extremal Point Probability Function Probabilistic Logic 
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.


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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Rolf Haenni
    • 1
  • Jan-Willem Romeijn
    • 2
  • Gregory Wheeler
    • 3
  • Jon Williamson
    • 4
  1. 1.Department of Engineering and Information TechnologyBern University of Applied SciencesBielSwitzerland
  2. 2.Faculty of PhilosophyUniversity of GroningenGroningenNetherlands
  3. 3.Universidade Nova de Lisboa, New University of LisbonCaparicaPortugal
  4. 4.School of European Culture & Languages Sec. PhilosophyUniversity of KentCanterburyUK

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