Cybernetics and Systems Analysis

, Volume 49, Issue 1, pp 36–40 | Cite as

Properties of separation procedures for discrete objects in Bayesian network models



It is shown that for discrete objects constructed on bounded samples there are examples that support the faithfulness assumption and examples for which it fails. Thus, the properties of separation procedures for continuous models do not hold for discrete objects.


separation Bayesian network Markov condition discrete object 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    J. Pearl, Causality: Models, Reasoning, and Inference, Cambridge Univ. Press (2000).Google Scholar
  2. 2.
    P. Spirtes, C. Glymour, and R. Scheines, Causation, Prediction, and Search, MIT Press, New York (2001).MATHGoogle Scholar
  3. 3.
    R. E. Neapolitan, Learning Bayesian Networks, Prentice Hall, NJ, Upper Saddle River (2004).Google Scholar
  4. 4.
    I. V. Sergienko, A. M. Gupal, and A. A. Vagis, “Symmetry in encoding genetic information in DNA,” Cybern. Syst. Analysis, 47, No. 3, 408–414 (2011).MathSciNetCrossRefGoogle Scholar
  5. 5.
    I. V. Sergienko, A. M. Gupal, and A. A. Vagis, “Symmetry and properties of encoding information in DNA,” Dokl. NANU, Vol. 439, No. 1, 30–32 (2011).MathSciNetGoogle Scholar
  6. 6.
    V. Boss, Lectures in Mathematics, Vol. 12, Counterexamples and Paradoxes. A Handbook, Knizhnyi Dom Librokom, Moscow (2009).Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.V. M. Glushkov Institute of CyberneticsNational Academy of Sciences of UkraineKyivUkraine

Personalised recommendations