Advertisement

An Algorithm for Parallel Tree-Shaped Bayesian Network Construction

  • Mieczysław A. Kłopotek
  • Justin Lindsey
Part of the Studies in Computational Intelligence book series (SCI, volume 369)

Abstract

The paper presents the way that Tree-shaped Bayesian network generation program has been developed for a share-nothing parallel machine. We point at the changes in Chow/Liu [2] algorithm that are possible within a parallel machine environment.

Keywords

Bayesian networks machine learning parallel computation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Chow, C., Wagner, T.: Consistency of an estimate of tree-dependent probability distribution. IEEE Transactions on Information Theory 18, 369–371 (1973)CrossRefGoogle Scholar
  2. 2.
    Chow, C., Liu, C.: Approximating discrete probability distributions with dependence trees. IEEE Transactions on Information Theory 14(3), 462–467 (1968)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Kłopotek, M.A.: A new bayesian tree learning method with reduced time and space complexity. Fundamenta Informaticae 49(4), 349–367 (2002)MathSciNetMATHGoogle Scholar
  4. 4.
    Kłopotek, M.A.: A new space-saving bayesian tree construction method for high dimensional data. Demonstratio Mathematica 35(3), 671–684 (2002)Google Scholar
  5. 5.
    Meila, M.: An Accelerated Chow and Liu Algorithm: Fitting Tree Distributions to High-Dimensional Sparse Data. In: Proceedings of the Sixteenth International Conference on Machine Learning, ICML 1999, pp. 249–257. Morgan Kaufmann Publishers Inc., San Francisco (1999)Google Scholar
  6. 6.
    Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. The MIT Press, Cambridge (2001)MATHGoogle Scholar
  7. 7.
    Valiveti, R.S., Oommen, B.J.: The use of chi-squared statistics in determining dependence tree, technical report scs-tr-153, School of Computer Science,Carleton University, ottawa, ontario (March 1989)Google Scholar
  8. 8.
    Valiveti, R.S., Oommen, B.J.: On using the chi-squared metric for determining stochastic dependence. Pattern Recognition 25(11), 1389–1400 (1992)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mieczysław A. Kłopotek
    • 1
    • 2
  • Justin Lindsey
    • 2
  1. 1.Institute of Computer SciencePolish Academy of SciencesWarszawaPoland
  2. 2.Netezza Corporation (an IBM Company)USA

Personalised recommendations