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Cycles in Bayesian Networks

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EN
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EN
The article is devoted to some critical problems of using Bayesian networks for solving practical problems, in which graph models contain directed cycles. The strict requirement of the acyclicity of the directed graph representing the Bayesian network does not allow to efficiently solve most of the problems that contain directed cycles. The modern theory of Bayesian networks prohibits the use of directed cycles. The requirement of acyclicity of the graph can significantly simplify the general theory of Bayesian networks, significantly simplify the development of algorithms and their implementation in program code for calculations in Bayesian networks.
Twórcy
  • Institute of Information and Computational Technology, 050010 Almaty, Kazakhstan and Al-Farabi Kazakh National University, Almaty, Kazakhstan
  • Information and Computational Technology, 050010 Almaty, Kazakhstan
  • Institute of Information and Computational Technology, 050010 Almaty, Kazakhstan and Al-Farabi Kazakh National University, Almaty, Kazakhstan
  • Institute of Information and Computational Technologies CS MES RK, Almaty and Lublin Technical University, Poland
  • Kazakh University Ways of Communications, Kazakhstan
Bibliografia
  • [1] A. Nafalski and A.P. Wibawa, “Machine translation with javanese speech levels’ classification,” Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, vol. 6, no 1, pp 21-25, 2016. https://doi.org/10.5604/20830157.1194260
  • [2] Z. Omiotek and P. Prokop, “The construction of the feature vector in the diagnosis of sarcoidosis based on the fractal analysis of CT chest images,” Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, vol. 9, no. 2, pp. 16-23, 2019. https://doi.org/10.5604/01.3001.0013.2541
  • [3] A. Litvinenko, O. Mamyrbayev, N. Litvinenko, A. Shayakhmetova, “Application of Bayesian networks for estimation of individual psychological characteristics,” Przegląd Elektrotechniczny, vol. 95, no. 5, pp. 92-97, 2019
  • [4] X.Q. Cai, X.Y. Wu, X. Zhou, “Stochastic scheduling subject to breakdown-repeat breakdowns with incomplete information,” Operations Research, vol. 57, no. 5, pp. 1236–1249, 2009. doi: 10.1287/opre.1080.0660
  • [5] K.W. Fornalski, “The Tadpole Bayesian Model for Detecting Trend Changes in Financial Quotations,” R&R Journal of Statistics and Mathematical Sciences, vol. 2, no. 1, pp. 117–122, 2016.
  • [6] J. Pearl “Artificial Intelligence Applications”, in How to Do with Probabilities what people say you can't,/ Editor Weisbin C.R., IEEE, North Holland, pp. 6–12, 1985.
  • [7] J. Pearl “Probabilistic Reasoning in Intelligent Systems”. San Francisco: Morgan Kaufmann Publishers, 1988,
  • [8] A. Tulupiev “Algebraic Bayesian networks,” in “Logical-probabilistic approach to modeling knowledge bases with uncertainty,” SPb.: SPIIRAS, 2000.
  • [9] S. Nikolenko, A. Tulupiev “The simplest cycles in Bayesian networks: Probability distribution and the possibility of its contradictory assignment,” SPIIRAS. Edition 2, 2004. vol.1.
  • [10] F.V. Jensen, T.D. Nielsen “Bayesian Networks and Decision Graphs,” Springer, 2007.
  • [11] D. Barber, “Bayesian Reasoning and Machine Learning,” 2017, 686 p. http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/020217.pdf
  • [12] R.E. Neapolitan “Learning Bayesian Networks,” 704p. http://www.cs.technion.ac.il/~dang/books/Learning%20Bayesian%20Net works(Neapolitan,%20Richard).pdf
  • [13] O. Mamyrbayev, M. Turdalyuly, N. Mekebayev, and et al. “Continuous speech recognition of kazakh language», AMCSE 2018 Int. conf. On Applied Mathematics, Computational Science and Systems Engineering, Rom, Italy, 2019, vol. 24, pp. 1-6.
  • [14] A. Litvinenko, N. Litvinenko, O. Mamyrbayev, A. Shayakhmetova, M. Turdalyuly “Clusterization by the K-means method when K is unknown,” Inter. conf. Applied Mathematics, Computational Science and Systems Engineering. Rome, Italy, 2019, vol. 24, pp. 1-6.
  • [15] O. Ore “Graph theory,” Мoscow: Science, 1980, 336 p.
  • [16] Ph. Kharari “Graph theory,” Мoscow: Mir, 1973, 300 p.
  • [17] V. Gmurman “Theory of Probability and Mathematical Statistics: Tutorial,” Moscow: 2003, 479 p.
  • [18] A.N. Kolmogorov “Theory: Manual,” in “Basic Concepts of Probability,” Moscow: Science, 1974.
  • [19] N. Litvinenko, A. Litvinenko, O. Mamyrbayev, A. Shayakhmetova “Work with Bayesian Networks in BAYESIALAB,” Almaty: IPIC, 2018, 311 p. (in Rus). ISBN 978-601-332-206-3.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
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bwmeta1.element.baztech-255fc064-12c5-4e6d-9b29-ad41251138dc
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