Abstract
Algebraic Bayesian networks belong to the class of probabilistic graphical models. They are represented as non-directional graphs with models of knowledge patterns in the nodes (KP). Each knowledge pattern contains closely related information about the subject domain, formalized in the form of an ideal of conjuncts or a set of quanta with truth probability estimates. In order to optimize the complexity of KPs through the use of scalar estimates, an approach to finding the canonical representation of KPs has previously been proposed. In this paper, the process of obtaining a canonical representation of the entire algebraic Bayesian network is proposed and studied for the first time. As a result, methods have been described that create a canonical representation based on the comprehensive KP and using chain generation. The results of this paper allow to reduce the time for calculating probability estimates in a priori inference by obtaining scalar estimates instead of interval estimates, which can be used to compute prior probability estimates or in systems where obtaining scalar probability estimates is preferable.
This work was performed within the framework of the project under the state assignment of the St. Petersburg Federal Research Center of the Russian Academy of Sciences No FFZF-2022-0003.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ding, Y.-J., Wang, Z.-C., Chen, G., Ren, W.-X., Xin, Y.: Markov Chain Monte Carlo-based Bayesian method for nonlinear stochastic model updating. J. Sound Vib. 520, 116696 (2022)
Xu, R., Liu, S., Zhang, Q., Yang, Z., Liu, J.: PEWOBS: an efficient Bayesian network learning approach based on permutation and extensible ordering-based search. Futur. Gener. Comput. Syst. 128, 505–520 (2022)
Gómez-Olmedo, M., Cabañas, R., Cano, A., Moral, S., Retamero, O.P.: Value-based potentials: exploiting quantitative information regularity patterns in probabilistic graphical models. Int. J. Intell. Syst. 36, 6913–6943 (2021)
De la Parra, C., Guntoro, A., Kumar, A.: Improving approximate neural networks for perception tasks through specialized optimization. Futur. Gener. Comput. Syst. 113, 597–606 (2020)
Hancock, J.T., Khoshgoftaar, T.M.: Survey on categorical data for neural networks. J. Big Data 7(1), 1–41 (2020)
Petrolo, M., Carrera, E.: On the use of neural networks to evaluate performances of shell models for composites. Adv. Model. Simul. Eng. Sci. 7, 1–28 (2020)
Wang, Y., Wu, A., Li, B.: Synchronization of bidirection multiple neural networks with impulsive coupling control. Adv. Difference Equ. 2020(1), 1–22 (2020)
Zhang, J., Huang, C.: Dynamics analysis on a class of delayed neural networks involving inertial terms. Adv. Difference Equ. 2020(1), 1–12 (2020)
Forio, M.A.E., et al.: A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates. Sci. Total Environ. 180, 152146 (2022)
Pérez, S., German-Labaume, C., Mathiot, S., Goix, S., Chamaret, P.: Using Bayesian networks for environmental health risk assessment. Environ. Res. 204, 112059 (2022)
Abaei, M.M., Hekkenberg, R., BahooToroody, A., Banda, O.V., van Gelder, P.: A probabilistic model to evaluate the resilience of unattended machinery plants in autonomous ships. Reliab. Eng. Syst. Saf. 219, 108176 (2022)
Niu, G., Wang, X., Liu, E., Zhang, B.: Lebesgue sampling based deep belief network for lithium-ion battery diagnosis and prognosis. IEEE Trans. Industr. Electron. 69, 8481–8490 (2022)
Zio, E., Mustafayeva, M., Montanaro, A.: A Bayesian belief network model for the risk assessment and management of premature screen-out during hydraulic fracturing. Reliab. Eng. Syst. Saf. 218, 108094 (2022)
Burström, G., Edström, E., Elmi-Terander, A.: Foundations of Bayesian learning in clinical neuroscience. Acta Neurochir. Suppl. 134, 75–78 (2022)
Liang, R., Liu, F., Liu, J.: A belief network reasoning framework for fault localization in communication networks. Sensors (Switzerland) 20(3), 1–21 (2020)
Steijn, W.M.P., Van Kampen, J.N., Van der Beek, D., Groeneweg, J., Van Gelder, P.H.A.J.M.: An integration of human factors into quantitative risk analysis using Bayesian Belief Networks towards developing a ‘QRA+’. Saf. Sci. 122, 104514 (2020)
Dag, A.Z., Akcam, Z., Kibis, E., Simsek, S., Delen, D.: A probabilistic data analytics methodology based on Bayesian Belief network for predicting and understanding breast cancer survival. Knowl.-Based Syst. 242, 108407 (2022)
Kharitonov, N.A., Tulupyev, A.L.: Algebraic Bayesian networks: the generation of the knowledge pattern canonical representation. In: Proceedings of 2021 24th International Conference on Soft Computing and Measurements, SCM 2021, pp. 144–146 (2021)
Tulupyev, A.L., Nikolenko, S.I., Sirotkin, A.V.: Bayesian Belief Networks: Probabilisticlogic Approach. SPb.: Nauka, Saint-Petersburg, Russia (2006, in Russian)
Tulupyev, A.L., Sirotkin, A.V., Nikolenko, S.I.: Bayesian Belief Networks. SPbSU Press, Saint-Petersburg, Russia (2009, in Russian)
Nilsson, N.J.: Probabilistic Logic. Artificial Intelligence. Elsevier Science Publishers BV, Amsterdam (1986)
Zolotin, A.A., Malchevskaya, E.A., Kharitonov, N.A., Tulupyev, A.L.: Local and global logical-probabilistic inference in the Algebraic Bayesian networks: matrix-vector description and the sensitivity questions. In: Fuzzy Systems and Soft Calculations, pp. 133–150 (2017, in Russian)
Zolotin, A.A., Tulupyev, A.L.: Sensitivity statistical estimates for local a posteriori inference matrix-vector equations in algebraic Bayesian networks over quantum propositions. Vestnik St. Petersburg Univ.-Math. 51(1), 42–48 (2018)
Tulupyev, A.L.: Algebraic Bayesian Networks: Global Logical and Probabilistic Inference in Joint Trees, 40 p. SPb.: Anatolia Publishing House LLC. (2007, in Russian)
Tulupyev, A.L.: Composition of distributions of random binary sequences. Inf. Technol. Intell. Methods 1, 105–112 (1996)
Khlobystova, A.O, Abramov, M.V.: Adaptation of the multi-pass social engineering attack model taking into account informational influence. In: Proceedings of 2021 24th International Conference on Soft Computing and Measurements (SCM 2021), pp. 65–68. (2021)
Khlobystova, A.O, Abramov, M.V.: The models separation of access rights of users to critical documents of information system as factor of reduce impact of successful social engineering attacks. In: Russian Advances in Fuzzy Systems and Soft Computing: Selected Contributions to the 8th International Conference on “Fuzzy Systems, Soft Computing and Intelligent Technologies (FSSCIT 2020)”, Smolensk, Russia, 29 June–1 July, vol. 2782, pp. 264–268 (2020)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Kharitonov, N., Vyatkin, A., Tulupyev, A. (2023). Algebraic Bayesian Networks: The Generation of the Network Canonical Representation. In: Kovalev, S., Kotenko, I., Sukhanov, A. (eds) Proceedings of the Seventh International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’23). IITI 2023. Lecture Notes in Networks and Systems, vol 777. Springer, Cham. https://doi.org/10.1007/978-3-031-43792-2_2
Download citation
DOI: https://doi.org/10.1007/978-3-031-43792-2_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43791-5
Online ISBN: 978-3-031-43792-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)