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Journal of Automation and Information Sciences
SJR: 0.275 SNIP: 0.59 CiteScore™: 0.8

ISSN Druckformat: 1064-2315
ISSN Online: 2163-9337

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Journal of Automation and Information Sciences

DOI: 10.1615/JAutomatInfScien.v44.i10.50
pages 60-68

Properties of Separation Procedures in the Models of Bayesian Networks

Alexandra A. Vagis
V.M. Glushkov Institute of Cybernetics of National Academy of Sciences of Ukraine, Kiev, Ukraine
Artur S. Koval
Kiev department of Moscow Physico-technical Institute of Physico-technical Scientific and Training Center of National Academy of Sciences of Ukraine

ABSTRAKT

It is shown that for discrete objects, constructed on limited samples, there are examples that confirm both the faithfulness assumption, and examples for which they are not fulfilled. Thus, the properties of separation procedures for the continuous models do not hold for discrete objects.

REFERENZEN

  1. Pearl J. , Causality: models, reasoning, and inference, Cambridge Univ.

  2. Spirtes P., Glymour C, Scheines R. , Causation, prediction and search (2nd ed.).

  3. Neapolitan R.E. , Learning Bayesian networks, Prentice Hall, Upper Saddle River, 2004.

  4. Sergienko I.V., Gupal A.M., Vagis A.A. , Symmetry in encoding genetic information in DNA.

  5. Sergienko I.V., Gupal A.M., Vagis A.A. , Symmetry and properties of the recording information in DNA.

  6. Boss V. , Lectures on mathematics, V. 12, Countrexamples and paradoxes [in Russian].


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