Algebraic Bayesian Networks: Parallel Algorithms for Maintaining Local Consistency

  • Nikita A. Kharitonov
  • Anatolii G. MaksimovEmail author
  • Alexander L. Tulupyev
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1156)


Algebraic Bayesian networks belong to the class of machine-learning probabilistic graphical models. One of the main tasks during researching machine learning models is the optimization of their time of work. This paper presents approaches to parallelizing algorithms for maintaining local consistency in algebraic Bayesian networks as one of the ways to optimize their time of work. An experiment provided to compare the time of parallel and nonparallel realizations of algorithms for maintaining local consistency.


Algebraic Bayesian networks Probabilistic graphic models Consistency Parallel computing Knowledge pattern Machine learning Bayesian networks Probabilistic-logical inference 



The research was carried out in the framework of the project on SPIIRAS governmental assignment No. 0073-2019-0003, with the financial support of the RFBR (project No. 18-01-00626: Methods of representation, synthesis of truth estimates and machine learning in algebraic Bayesian networks and related knowledge models with uncertainty: the logic-probability approach and graph systems).


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.St. Petersburg Institute for Informatics and Automation of the Russian Academy of SciencesSt. PetersburgRussia
  2. 2.St. Petersburg State UniversitySt. PetersburgRussia

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