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Bayesian and information-theoretic priors for Bayesian network parameters

  • Petri Kontkanen
  • Petri Myllymäki
  • Tomi Silander
  • Henry Tirri
  • Peter Grünwalde
Bayesian Networks
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1398)

Abstract

We consider Bayesian and information-theoretic approaches for determining non-informative prior distributions in a parametric model family. The information-theoretic approaches are based on the recently modified definition of stochastic complexity by Rissanen, and on the Minimum Message Length (MML) approach by Wallace. The Bayesian alternatives include the uniform prior, and the equivalent sample size priors. In order to be able to empirically compare the different approaches in practice, the methods are instantiated for a model family of practical importance, the family of Bayesian networks.

Keywords

Bayesian Network Prior Distribution Dirichlet Form Predictive Distribution Fisher Information Matrix 
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-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  • Petri Kontkanen
    • 1
  • Petri Myllymäki
    • 1
  • Tomi Silander
    • 1
  • Henry Tirri
    • 1
  • Peter Grünwalde
    • 2
  1. 1.Complex Systems Computation Group (CoSCo) P.O.Box 26, Department of Computer ScienceFIN-00014 University of HelsinkiFinland
  2. 2.Department of Algorithms and ArchitecturesCWINetherlands

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