Algebraic Bayesian Networks: The Use of Parallel Computing While Maintaining Various Degrees of Consistency

  • Nikita A. KharitonovEmail author
  • Anatoly G. Maximov
  • Alexander L. Tulupyev
Conference paper
Part of the Studies in Systems, Decision and Control book series (SSDC, volume 199)


This paper presents approaches to parallelization of algorithms for maintaining external and internal consistency in algebraic Bayesian networks as one of the representatives of probabilistic graphical models. The algorithms modified based on these approaches are described and presented in the form of schemes.


Algebraic Bayesian networks Probabilistic graphical models Consistency External consistency Internal consistency Parallel computing 


  1. 1.
    Bloom, F: Optimizing decision making. In: Opportunities in Neuroscience for Future Army Applications, pp. 36–44. The National Academies Press, Washington, DC (2009)Google Scholar
  2. 2.
    Das, M., Ghosh, S.K.: FB-STEP: a fuzzy Bayesian network based data-driven framework for spatio-temporal prediction of climatological time series data. Expert Syst. Appl. 117, 211–227 (2019). Scholar
  3. 3.
    Falzer, P.R., Garman, D.M.: Optimizing clozapine through clinical decision making. Acta Psychiatr. Scand. 126(1), 47–58 (2012). Scholar
  4. 4.
    Fehlings, M.G., Noonan, V.K., Atkins, D., Burns, A.S., Cheng, C.L., Singh, A., Dvorak, M.F.: Optimizing clinical decision making in acute traumatic spinal cord injury. J. Neurotrauma 34(20), 2841–2842 (2017). Scholar
  5. 5.
    Guzmán, E., Vázquez, M., Del Valle, D., Pérez-Rodríguez, P.: Artificial neuronal networks: a Bayesian approach using parallel computing. Rev. Colomb. Estad. 41(2), 173–189 (2018). Scholar
  6. 6.
    Gan, H.X., Zhang, Y., Song, Q.: Bayesian belief network for positive unlabeled learning with uncertainty. Pattern. Recogn. Lett. 90, 28–35 (2017). Scholar
  7. 7.
    Hosseini, S., Sarder, M.D.: Development of a Bayesian network model for optimal site selection of electric vehicle charging station. Int. J. Electr. Power 105, 110–122 (2019). Scholar
  8. 8.
    Ibrahimovic, S., Turulja, L., Bajgoric, N.: Bayesian belief networks in IT investment decision making. In: Maximizing Information System Availability Through Bayesian Belief Network Approaches: Emerging Research and Opportunities, pp. 75–107 (2017).
  9. 9.
    Kharitonov, N.A., Zolotin, A.A., Tulupyev, A.L.: Software implementation of algebraic Bayesian networks consistency algorithms. In: 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM), Saint-Petersburg, Russia, pp. 8–10 (2017)Google Scholar
  10. 10.
    Kulagin, V.: Design of control systems for parallel computing structures based on net models. In: 2016 International Siberian Conference on Control and Communications (SIBCON), Moscow, Russia, pp. 1–4 (2016).
  11. 11.
    Kulagin, V.P.: Problems of parallel computing. Prospects Sci. Educ. 1(19) (2016). (in Russian)Google Scholar
  12. 12.
    Li, J., Song, G., Semakula, H.M., Zhang, S.: Climatic burden of eating at home against away-from-home: a novel Bayesian belief network model for the mechanism of eating-out in urban China. Sci. Total Environ. 650, 224–232 (2019). Scholar
  13. 13.
    Quintanilha, A.: Knowledge and dialogue to deal with uncertainty. Free Radical Bio. Med. 106, S4–S4 (2018). Scholar
  14. 14.
    Sreelekha, S.: NeuroSymbolic integration with uncertainty. Ann. Math. Artif. Intel. 106(3–4), 201–220 (2018). Scholar
  15. 15.
    Suleimanov, A., Abramov, M., Tulupyev, A.: Modelling of the social engineering attacks based on social graph of employees communications analysis. In: Proceedings—2018 IEEE Industrial Cyber-Physical Systems, ICPS 2018, pp. 801–805. IEEE (2018).
  16. 16.
    Tulupyev, A.L.: Algebraic Bayesian networks: a probabilistic-logic graphical model of knowledge patterns bases with uncertainty. Doctor of science dissertation. St. Petersburg State University (2009). (in Russian)Google Scholar
  17. 17.
    Vasimuddin, M., Chockalingam, S.P., Aluru, S.: A parallel algorithm for Bayesian network inference using arithmetic circuits. In: Proceedings—2018 IEEE 32nd International Parallel and Distributed Processing Symposium, IPDPS 2018, pp. 34–43. IEEE (2018).
  18. 18.
    Zhang, M.M., Lam, H., Lin, L.: Robust and parallel Bayesian model selection. Comput. Stat. Data Anal. 127, 229–247 (2018). Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.St. Petersburg Institute for Informatics and Automation of the Russian Academy of Sciences (SPIIRAS)St. PetersburgRussia

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