Abstract
Given finitely many events in a probability space, conditional independences among the indicators of events are considered simultaneously with the signs of covariances. Resulting discrete structures are studied restricting attention mostly to all couples and triples of events. Necessary and sufficient conditions for such structures to be represented by events are found. Consequences of the results for the patterns of conjunctive forks are discussed.
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References
V. Chvátal, F. Matúš and Y. Zwólš, Patterns of conjunctive forks, arXiv:1608.03949 [math.PR] (2016).
Dawid A.P.: Conditional independence in statistical theory (with discussion). J. R. Statist. Soc. B 41, 1–31 (1979)
A. Deitmar, A First Course in Harmonic Analysis, Springer-Verlag (New York, 2015).
M. Drton, B. Sturmfels and S. Sullivant, Lectures on Algebraic Statistics, Oberwolfach Seminars, Birkhäuser Applied Probability and Statistics, Birkhäuser (2009).
Drton M., Xiao H.: Smoothness of Gaussian conditional independence models. Contemp. Math. 516, 155–177 (2010)
P. F. Lazarsfeld and N. W. Henry, Latent Structure Analysis, Houghton Mifflin Company (Boston, 1968).
Lněnička R., Matúš F.: On Gaussian conditional independence structures. Kybernetika 43, 327–342 (2007)
Matúš F.: Stochastic independence, algebraic independence and abstract connectedness. Theoret. Comput. Sci. 134, 455–471 (1994)
Matúš F.: Conditional independences among four random variables III: final conclusion. Combin. Probab. Comput. 8, 269–276 (1999)
O. Kallenberg, Foundations of Modern Probability, Springer-Verlag (New York, 1997).
S. Lauritzen and K. Sadeghi, Unifying Markov properties for graphical models. arXiv:1608.05810v3 [math.ST] (2017).
Pitcher E., Smiley M.F.: Transitivities of betweenness. Trans. Amer. Math. Soc. 52, 95–114 (1942)
H. Reichenbach, The Direction of Time, University of California Press (Berkeley, Los Angeles, CA, 1956).
M. Studený, Probabilistic Conditional Independence Structures, Springer-Verlag (New York, 2005).
Sullivant S.: Gaussian conditional independence has no finite complete axiom system. J. Pure Appl. Algebra 213, 1502–1506 (2009)
P. Šimeček, Classes of Gaussian, discrete and binary representable independence models have no finite characterization, in: Prague Stochastics (M. Hušková and M. Janžura, eds.), Matfyzpress, Charles University (Prague, 2006), pp. 622–632.
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This research was supported by Grant Agency of the Czech Republic, Grant 16-12010S.
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Matúš, F. On patterns of conditional independences and covariance signs among binary variables. Acta Math. Hungar. 154, 511–524 (2018). https://doi.org/10.1007/s10474-018-0799-6
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DOI: https://doi.org/10.1007/s10474-018-0799-6
Key words and phrases
- conditional independence
- covariance
- correlation
- binary variable
- ternary relation
- conjunctive fork
- forkness
- betweenness
- semigraphoid