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Totally Ordered Conditional Independence Models

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A subclass of lattice conditional independence models is introduced. The new class of models is called totally ordered independence models. The class is based on an assumption that the index set which orders the random variables is a chain. It is shown that there is a jump in the chain if and only if there is a conditional independence relation. Some comparisons between the lattice conditional independence models and totally ordered independence models are presented.

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References

  1. S. A. Andersson, D. Madigan, M. D. Perlman, and Ch. M. Triggs, “On the relation between conditional independence models determined by finite distributive lattices and by directed acyclic graphs”, J. Statist. Plann. Inf., 48, 25–46 (1995).

    Article  MathSciNet  MATH  Google Scholar 

  2. S. A. Andersson, J. I. Marden, and M. D. Perlman, “Totally ordered multivariate linear models,” Sankhyā Ser. A, 370–394 (1993).

  3. S. A. Andersson and M. D. Perlman, “Lattice models for conditional independence in a multivariate normal distribution,” Ann, Statist., 1318–1358 (1993).

  4. S. A. Andersson and M. D. Perlman, “Normal linear models with lattice conditional independence restrictions,” Multivariate analysis and its applications (Hong Kong, 1992), 97–110, IMS Lecture Notes Monogr. Ser., 24, Institute of Mathematical Statistics, Hayward, CA, 1994.

  5. S. A. Andersson and M. D. Perlman, “Normal linear regression models with recursive graphical Markov structure,” J. Multivar. Anal., 66, 133–187 (1998).

    Article  MathSciNet  MATH  Google Scholar 

  6. G. Grätzer, General Lattice Theory. With appendices by B. A. Davey, R. Freese, B. Ganter, M. Greferath, P. Jipsen, H. A. Priestley, H. Rose, E. T. Schmidt, S. E. Schmidt, F. Wehrung and R. Wille. Reprint of the 1998 second edition. Birkhäuser Verlag, Basel, 2003.

  7. S. L. Lauritzen, “Graphical models,” Oxford Statistical Science Series, 17. Oxford Science Publications. The Clarendon Press, Oxford University Press, New York, 1996.

  8. S. L. Lauritzen and Th. S. Richardson, “Chain graph models and their causal interpretations,” J. Royal Statist. Soc. Ser. B Statistical Methodology, 64, 321–361 (2002).

  9. T. S. Richardson, “A characterization of Markov equivalence for directed cyclic graphs. 1996 Uncertainty in AI (UAI ’96) Conference,” Inter. J. Approx. Reasoning, 17, 107–162 (1997).

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Correspondence to N. Gauraha.

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Published in Zapiski Nauchnykh Seminarov POMI, Vol. 501, 2021, pp. 102–117.

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Gauraha, N., von Rosen, D. Totally Ordered Conditional Independence Models. J Math Sci 273, 722–731 (2023). https://doi.org/10.1007/s10958-023-06535-6

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  • DOI: https://doi.org/10.1007/s10958-023-06535-6

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