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A Formalism for Building Causal Polytree Structures Using Data Distributions

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Book cover Foundations of Intelligent Systems (ISMIS 2000)

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Abstract

In this paper we have considered the problem of approximating an underlying distribution by one derived from a dependence polytree. This paper proposes a formal and systematic algorithm, which traverses the undirected tree obtained by the Chow method [2], and by using the independence tests it successfully orients the polytree. Our algorithm uses an application of the Depth First Search (DFS) strategy to multiple causal basins. The algorithm has been formally proven and rigorously tested for synthetic and real-life data.

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References

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Author information

Authors and Affiliations

  1. School of Information Technology and Engineering, University of Ottawa, K1S 5B6, Ottawa, Canada

    M. Ouerd & S. Matwin

  2. School of Computer Science, Carleton University, K1S 5B6, Ottawa, Canada

    B. J. Oommen

Authors
  1. M. Ouerd
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  2. B. J. Oommen
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  3. S. Matwin
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Editor information

Editors and Affiliations

  1. Institute of Computer Science, Polish Academy of Sciences, 01-237, Warsaw, Poland

    Zbigniew W. Raś

  2. Department of Computer Science, University of North Carolina, NC 28223, Charlotte, USA

    Zbigniew W. Raś

  3. Department of Science and Technology, Waseda University, 61-414, 3-4-1, Ohkubo, Shinjuku-ku, 169-8555, Tokyo, Japan

    Setsuo Ohsuga

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© 2000 Springer-Verlag Berlin Heidelberg

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Ouerd, M., Oommen, B.J., Matwin, S. (2000). A Formalism for Building Causal Polytree Structures Using Data Distributions. In: Raś, Z.W., Ohsuga, S. (eds) Foundations of Intelligent Systems. ISMIS 2000. Lecture Notes in Computer Science(), vol 1932. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39963-1_66

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  • DOI: https://doi.org/10.1007/3-540-39963-1_66

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41094-2

  • Online ISBN: 978-3-540-39963-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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