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Acyclic Directed Graphs to Represent Conditional Independence Models

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Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2009)

Abstract

In this paper we consider conditional independence models closed under graphoid properties. We investigate their representation by means of acyclic directed graphs (DAG). A new algorithm to build a DAG, given an ordering among random variables, is described and peculiarities and advantages of this approach are discussed. Finally, some properties ensuring the existence of perfect maps are provided. These conditions can be used to define a procedure able to find a perfect map for some classes of independence models.

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References

  1. Baioletti, M., Busanello, G., Vantaggi, B.: Algorithms for the closure of graphoid structures. In: Proc. of 12th Inter. Conf. IPMU 2008, Malaga, pp. 930–937 (2008)

    Google Scholar 

  2. Baioletti, M., Busanello, G., Vantaggi, B.: Conditional independence structure and its closure: inferential rules and algorithms. Accepted for Publication in International Journal of Approximate Reasoning (2008)

    Google Scholar 

  3. Baioletti, M., Busanello, G., Vantaggi, B.: Conditional independence structure and its closure: inferential rules and algorithms. In: Technical Report, 5/2009 of University of Perugia (2009)

    Google Scholar 

  4. Coletti, G., Scozzafava, R.: Zero probabilities in stochastical independence. In: Bouchon- Meunier, B., Yager, R.R., Zadeh, L.A. (eds.) Information, Uncertainty, Fusion, pp. 185–196. Kluwer Academic Publishers, Dordrecht (2000)

    Chapter  Google Scholar 

  5. Coletti, G., Scozzafava, R.: Probabilistic logic in a coherent setting. Kluwer, Dordrecht (2002) (Trends in logic n.15)

    Book  MATH  Google Scholar 

  6. Dawid, A.P.: Conditional independence in statistical theory. J. Roy. Stat. Soc. B 41, 15–31 (1979)

    MATH  Google Scholar 

  7. Jensen, F.V.: An Introduction to bayesian Networks. UCL Press/ Springer Verlag (1966)

    Google Scholar 

  8. Lauritzen, S.L.: Graphical models. Clarendon Press, Oxford (1996)

    MATH  Google Scholar 

  9. Pearl, J.: Probabilistic reasoning in intelligent systems: networks of plausible inference. Morgan Kaufmann, Los Altos (1988)

    MATH  Google Scholar 

  10. Studený, M.: Semigraphoids and structures of probabilistic conditional independence. Ann. Math. Artif. Intell. 21, 71–98 (1997)

    Article  MATH  Google Scholar 

  11. Studený, M.: Complexity of structural models. In: Proc. Prague Stochastics 1998, Prague, pp. 521–528 (1998)

    Google Scholar 

  12. Studený, M., Bouckaert, R.R.: On chain graph models for description of conditional independence structures. Ann. Statist. 26(4), 1434–1495 (1998)

    Article  MATH  Google Scholar 

  13. Vantaggi, B.: Conditional independence in a coherent setting. Ann. Math. Artif. Intell. 32, 287–313 (2001)

    Article  MATH  Google Scholar 

  14. Verma, T.S.: Causal networks: semantics and expressiveness. Technical Report R–65, Cognitive Systems Laboratory, University of California, Los Angeles (1986)

    Google Scholar 

  15. Witthaker, J.J.: Graphical models in applied multivariate statistic. Wiley & Sons, New York (1990)

    Google Scholar 

  16. Wong, S.K.M., Butz, C.J., Wu, D.: On the Implication Problem for Probabilistic Conditional Independency. IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans 30(6), 785–805 (2000)

    Article  Google Scholar 

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© 2009 Springer-Verlag Berlin Heidelberg

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Baioletti, M., Busanello, G., Vantaggi, B. (2009). Acyclic Directed Graphs to Represent Conditional Independence Models. In: Sossai, C., Chemello, G. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2009. Lecture Notes in Computer Science(), vol 5590. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02906-6_46

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  • DOI: https://doi.org/10.1007/978-3-642-02906-6_46

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-02905-9

  • Online ISBN: 978-3-642-02906-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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