Cybernetics and Systems Analysis

, Volume 44, Issue 2, pp 219–224 | Cite as

Methods of constructing Bayesian networks based on scoring functions

  • M. Z. Zgurovskii
  • P. I. Bidyuk
  • A. N. Terent’ev
Open Access


Available methods of constructing Bayesian networks with the use of scoring functions are analyzed. The Cooper-Herskovits and MDL functions are described in detail and used to compare algorithms of constructing Bayesian networks.


Bayesian network search and scoring method computational characteristics minimum description length 


  1. 1.
    C. K. Chow and C. N. Liu, “Approximating discrete probability distributions with dependence trees,” IEEE Trans. Inform. Theory, 4, No. 3, 462–467 (1968).CrossRefGoogle Scholar
  2. 2.
    G. Rebane and J. Pearl, “The recovery of causal poly-trees from statistical data,” Intern. J. Approx. Res., 2, No. 3, 175–182 (1988).Google Scholar
  3. 3.
    E. Herskovits and G. Cooper, “Kutato: an entropy-driven system for construction of probabilistic expert systems from databases,” in: Proc. 6th Intern. Conf. on Uncertainty in Artificial Intelligence (UAI’90), Cambridge, MA, USA, Elsevier Science, New York (1991), pp. 54–62.Google Scholar
  4. 4.
    P. Spirtes, C. Glymour, and R. Scheines, “From probability to causality,” in: Philos. Studies, 64, No. 1, Springer Netherlands, Amsterdam (1991), pp. 1–36.Google Scholar
  5. 5.
    G. Cooper and E. Herskovits, “A Bayesian method for the induction of probabilistic networks from data,” Machine Learning, 9, 309–347 (1992).zbMATHGoogle Scholar
  6. 6.
    W. Lam and F. Bacchus, “Learning Bayesian belief networks: an approach based on the MDL principle,” Computational Intelligence, 10, No. 4, 269–293 (1994).CrossRefGoogle Scholar
  7. 7.
    S. Acid and L. Campos, “Benedict: an algorithm for learning probabilistic belief networks,” in: Proc. 6th Intern. Conf. on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU’96), Granada, Spain, Springer, New York (1997), pp. 979–984.Google Scholar
  8. 8.
    M. Singh and M. Valtorta, “Construction of Bayesian network structures from data: a brief survey and an efficient algorithm,” Intern. J. Approx. Res., 12, 111–131 (1995).zbMATHCrossRefGoogle Scholar
  9. 9.
    N. Friedman and M. Goldszmidt, “Learning Bayesian networks with local structure,” in: Proc. 12th Intern. Conf. on Uncertainty in Artificial Intelligence (UAI’96), Portland, Oregon, USA, Morgan Kaufmann, SF (1996), pp. 252–262.Google Scholar
  10. 10.
    C. Wallace, K. Korb, and H. Dai, “Causal discovery via MML,” in: Proc. 13th Intern. Conf. on Machine Learning (ICML’96), Bari, Italy, Morgan Kaufmann, SF (1996), pp. 516–524.Google Scholar
  11. 11.
    J. Suzuki, “Learning Bayesian belief networks based on the MDL principle: an efficient algorithm using the branch and bound technique,” in: IEICE Trans. Inform. Syst. (1999), pp. 356–367.Google Scholar
  12. 12.
    A. N. Terent’ev and P. I. Bidyuk, “A heuristic method to construct Bayesian networks,” Mat. Mash. Sist., 3, 12–23 (2006).Google Scholar
  13. 13.
    R. Dechter, “Bucket elimination: a unifying framework for reasoning,” ACM Press, 28, No. 61, 1–51 (1996).Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2008

Authors and Affiliations

  • M. Z. Zgurovskii
    • 1
    • 2
  • P. I. Bidyuk
    • 1
  • A. N. Terent’ev
    • 2
  1. 1.National Technical University “Kyiv Polytechnical Institute”KyivUkraine
  2. 2.Institute of Applied Systems Analysis of the National Academy of Sciences of Ukraine and Ministry of Education and Science of UkraineKyivUkraine

Personalised recommendations