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Journal of Automation and Information Sciences

Published 12 issues per year

ISSN Print: 1064-2315

ISSN Online: 2163-9337

SJR: 0.173 SNIP: 0.588 CiteScore™:: 2

Indexed in

Bayesian Recognition Procedures on Networks

Volume 42, Issue 11, 2010, pp. 58-63
DOI: 10.1615/JAutomatInfScien.v42.i11.60
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ABSTRACT

The polynomial algorithms of determining Bayesian network structure are described as "tree" or "polytree". In the known Bayesian network structure consideration is given to Bayesian recognition procedures built on learning samples by estimations of transition probabilities.

REFERENCES
  1. Gupal A.M., Sergienko I.V., Optimal recognition procedures.

  2. Vapnik V.N., Chervonenkis A.Ya., Theory of images recognition.

  3. Pearl J., Probabilistic reasoning in intelligent systems: networks of plausible inference.

  4. Gupal A.M., Vagis A.A., Statistical estimation of the Markov pattern recognition procedure.

  5. Anderson T.W., Goodman L.A., Statistical inference about Markov chains.

  6. Sergienko I.V., Beletskiy B.A., Gupal A.M., Predicting torsion angles in amino acid protein sequences based on a Bayesian classification procedure on Markov chains.

CITED BY
  1. Aleardi Mattia, Vinciguerra Alessandro, Hojat Azadeh, A geostatistical Markov chain Monte Carlo inversion algorithm for electrical resistivity tomography, Near Surface Geophysics, 19, 1, 2021. Crossref

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