Algebraic Bayesian Networks: Parallel Algorithms for Maintaining Local Consistency
- 12 Downloads
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.
KeywordsAlgebraic 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).
- 4.Tulupyev, A.L., Nikolenko, S.I., Sirotkin, A.V.: Fundamentals of the Theory of Bayesian Networks: Textbook. St.-Petersburg University, Saint-Petersburg (2019). (in Russian)Google Scholar
- 8.Buscombe, D., Grams, P.E.: Probabilistic substrate classification with multispectral acoustic backscatter: a comparison of discriminative and generative models. Geosciences 8(11) (2018). Article no. UNSP395. https://doi.org/10.3390/geosciences8110395
- 14.Tai, W.P., Teng, Q.Y., Zhou, Y.M., Zhou, J.P., Wang, Z.: Chaos synchronization of stochastic reaction-diffusion time-delay neural networks via non-fragile output-feedback control. Appl. Math. Comput. 354, 115–127 (2019). https://doi.org/10.1016/j.amc.2019.02.028MathSciNetCrossRefzbMATHGoogle Scholar
- 15.Tulupyev, A.L.: Algebraic Bayesian Networks: Global Logical and Probabilistic Inference in Joint Trees: A Tutorial, 2nd edn. SPb: VVM, Saint-Petersburg (2019). (in Russian)Google Scholar
- 16.Tulupyev, A.L.: Algebraic Bayesian Networks: Local Logical and Probabilistic Inference: A Tutorial, 2nd edn. SPb: VVM, Saint-Petersburg (2019). (in Russian)Google Scholar
- 17.Kharitonov, N.A., Maximov, A.G., Tulupyev, A.L.: Algebraic Bayesian networks: the use of parallel computing while maintaining various degrees of consistency. Studies in Systems, Decision and Control, vol. 199, pp. 696–704 (2019). https://doi.org/10.1007/978-3-030-12072-6_56
- 19.Kharitonov N., Tulupyev A., Zolotin A.: Software implementation of reconciliation algorithms in algebraic Bayesian networks. In: Proceedings of 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM), pp. 8–10 (2017). https://doi.org/10.1109/SCM.2017.7970479
- 20.Mal’chevskaya, E.A., Berezin, A.I., Zolotin, A.A., Tulupyev, A.L.: Algebraic Bayesian networks: local probabilistic-logic inference machine architecture and set of minimal joint graphs. Advances in Intelligent Systems and Computing, vol. 451, pp. 69–79 (2016)Google Scholar
- 21.Abramov, M.V., Azarov, A.A.: Identifying user’s of social networks psychological features on the basis of their musical preferences. In: Proceedings of 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM), pp. 90–92 (2017). https://doi.org/10.1109/SCM.2017.7970504
- 22.Bagretsov, G.I., Shindarev, N.A., Abramov, M.V., Tulupyeva, T.V.: Approaches to development of models for text analysis of information in social network profiles in order to evaluate user’s vulnerabilities profile. In: Proceedings of 2017 XX IEEE International Conference on Soft Computing and Measurements (SCM), pp. 93–95 (2017). https://doi.org/10.1109/SCM.2017.7970505
- 23.Shindarev, N., Bagretsov, G., Abramov, M., Tulupyeva, T., Suvorova, A.: Approach to identifying of employees profiles in websites of social networks aimed to analyze social engineering vulnerabilities. Advances in Intelligent Systems and Computing, vol. 679, pp. 441–447 (2018)Google Scholar