Management in Information Systems Based on a Model Using the Bayesian Inference Apparatus Using Tikhonov Regularizing Functionals
( Pp. 91-101)
More about authors
Prokopchina Svetlana V.
Dr. Sci. (Eng.); Professor, Department of Artificial Intelligence, Faculty of Information Technology and Big Data Analysis
Financial University under the Government of the Russian Federation
Moscow, Russian Federation Zvyagin Leonid S. Cand. Sci. (Econ.), Associate Professor; associate professor, Department of Modeling and System Analysis, Faculty of Information Technology and Big Data Analysis; Financial University under the Government of the Russian Federation; Moscow, Russian Federation
Financial University under the Government of the Russian Federation
Moscow, Russian Federation Zvyagin Leonid S. Cand. Sci. (Econ.), Associate Professor; associate professor, Department of Modeling and System Analysis, Faculty of Information Technology and Big Data Analysis; Financial University under the Government of the Russian Federation; Moscow, Russian Federation
Abstract:
The article discusses the issue of improving management processes in organizational systems through the introduction of a regularizing Bayesian approach (RBP). The relevance of the research is due to the need to increase the stability of decisions made in conditions of high uncertainty and dynamic changes in the characteristics of information channels. A mathematical model has been developed that integrates a priori expert knowledge and current monitoring data through the Bayesian inference apparatus using Tikhonov regularizing functionals. The article describes an algorithm for the functioning of the system, including the formation of surrogate models based on Gaussian processes and optimization through the acquisition function. The results of the work were a synthesized intelligent control architecture that minimizes the entropy of the information system and meets the criteria of sustainability. Numerical experiments in twelve scenarios have confirmed the advantage of the proposed method over classical algorithms. The average performance increase was 17.4%, with a significant decrease in variance (to 0.045) and a stability coefficient of 0.94. The advantages of the approach in terms of working with small amounts of data and interpretability of results are formulated, as well as the limitations associated with computing capacity and expert dependence.
How to Cite:
Prokopchina S.V. and Zvyagin L.S. Management in information systems based on a model using the Bayesian inference apparatus using Tikhonov regularizing functionals. Computational Nanotechnology. 13, 1 (2026), 91–101. DOI: 10.33693/2313-223X-2026-13-1-91-101. EDN: MMZKTH
Reference list:
Voronov V.S., Davydov V.D. Bayesian approach in financial engineering: designing intelligent financial decision support systems. Russian Journal of Innovation Economics. 2021. Vol. 11. No. 4. Pp. 1509–1520. (In Rus.)
Dorozhko I.V., Gorokhov G.M., Kirillov I.A. Methodological approach to the development of a decision support system for an operator of an automated process control system based on dynamic Bayesian networks. MAI Proceedings. 2022. No. 125. Pp. 578–613. (In Rus.)
Dorozhko I.V., Zubachev A.M., Alexandrov M.A. Methodology for the development of an intelligent decision support system based on the apparatus of Bayesian networks. Problems of defense technology. Series 16: Technical Means of Countering Terrorism. 2024. No. 1–2 (187–188). Pp. 40–48. (In Rus.)
Zolotarevsky A.Yu. Analytics and decision support system for a small manufacturing enterprise based on a regularizing Bayesian approach. Soft Measurements and Computing. 2024. Vol. 75. No. 2-2. Pp. 38–48. (In Rus.)
Prokopchina S.V. Bayesian intelligent technologies in problems of distribution law modeling under uncertainty. Monograph. Moscow: Scientific Library, 2020. 291 p.
Prokopchina S.V. Bayesian intelligent technologies: Methodology and application in digitalization problems. In: Control systems and information technologies. 2019.
Prokopchina S.V. Intelligent sensor networks in Industry 5.0. A generalized concept for creating digital platforms for managing complex systems based on a regularizing Bayesian approach. In: International Conference on Soft Computing and Measurements. 2022. Vol. 1. Pp. 3–10.
Shevtsova Yu.V. Bayesian technologies: Their implementation in the Hugin software environment and application in operational risk management in telecommunications. A study guide. 2nd ed. Novosibirsk: Siberian State University of Telecommunications and Information Science, 2016. 90 p.
Cakmak S., Marban R.A., Frazier P., Zhou E. Bayesian optimization of risk measures. Advances in Neural Information Processing Systems. 2020. Vol. 33. Pp. 20130–20141.
Hernández-Lobato D., Hernandez-Lobato J., Shah A., Adams R. Predictive entropy search for multi-objective Bayesian optimization. International Conference on Machine Learning. 2016. Pp. 1492–1501.
Iwazaki S., Inatsu Y., Takeuchi I. Mean-variance analysis in Bayesian optimization under uncertainty. In: International Conference on Artificial Intelligence and Statistics. 2021. Pp. 973–981.
Jafar S.H. Financial applications of gaussian processes and Bayesian optimization. In: Bayesian Reasoning and Gaussian Processes for Machine Learning Applications. 2022. Pp. 111–122.
Kaufmann E., Cappé O., Garivier A. On Bayesian upper confidence bounds for bandit problems. Artificial Intelligence and Statistics. 2012. Vol. 22. Pp. 592–600.
Kerleguer B., Cannamela C., Garnier J. A Bayesian neural network approach to multi-fidelity surrogate modeling. International Journal for Uncertainty Quantification. 2024. Vol. 14. No. 1. Pp. 43–60.
Li J., Li M., Wu D. et al. A Bayesian networks-based risk identification approach for software process risk: The context of Chinese trustworthy software. International Journal of Information Technology & Decision Making. 2016. Vol. 15. No. 06. Pp. 1391–1412.
Lim Y.-F., Ng C.K., Vaitesswar U.S., Hippalgaonkar K. Extrapolative Bayesian optimization with Gaussian process and neural network ensemble surrogate models. Advanced Intelligent Systems. 2021. Vol. 3. No. 11. Art. 2100101.
You Z., Cartlidge J., Elliott K. et al. Improving Bayesian optimization for portfolio management with an adaptive scheduling. ArXiv Preprint. 2025. No. 2504.13529.
You Z., Cartlidge J., Elliott K. et al. Risk-aware black-box portfolio construction using Bayesian optimization with adaptive weighted Lagrangian estimator. ArXiv Preprint. 2025. No. 4. Pp. 1–15.
Dorozhko I.V., Gorokhov G.M., Kirillov I.A. Methodological approach to the development of a decision support system for an operator of an automated process control system based on dynamic Bayesian networks. MAI Proceedings. 2022. No. 125. Pp. 578–613. (In Rus.)
Dorozhko I.V., Zubachev A.M., Alexandrov M.A. Methodology for the development of an intelligent decision support system based on the apparatus of Bayesian networks. Problems of defense technology. Series 16: Technical Means of Countering Terrorism. 2024. No. 1–2 (187–188). Pp. 40–48. (In Rus.)
Zolotarevsky A.Yu. Analytics and decision support system for a small manufacturing enterprise based on a regularizing Bayesian approach. Soft Measurements and Computing. 2024. Vol. 75. No. 2-2. Pp. 38–48. (In Rus.)
Prokopchina S.V. Bayesian intelligent technologies in problems of distribution law modeling under uncertainty. Monograph. Moscow: Scientific Library, 2020. 291 p.
Prokopchina S.V. Bayesian intelligent technologies: Methodology and application in digitalization problems. In: Control systems and information technologies. 2019.
Prokopchina S.V. Intelligent sensor networks in Industry 5.0. A generalized concept for creating digital platforms for managing complex systems based on a regularizing Bayesian approach. In: International Conference on Soft Computing and Measurements. 2022. Vol. 1. Pp. 3–10.
Shevtsova Yu.V. Bayesian technologies: Their implementation in the Hugin software environment and application in operational risk management in telecommunications. A study guide. 2nd ed. Novosibirsk: Siberian State University of Telecommunications and Information Science, 2016. 90 p.
Cakmak S., Marban R.A., Frazier P., Zhou E. Bayesian optimization of risk measures. Advances in Neural Information Processing Systems. 2020. Vol. 33. Pp. 20130–20141.
Hernández-Lobato D., Hernandez-Lobato J., Shah A., Adams R. Predictive entropy search for multi-objective Bayesian optimization. International Conference on Machine Learning. 2016. Pp. 1492–1501.
Iwazaki S., Inatsu Y., Takeuchi I. Mean-variance analysis in Bayesian optimization under uncertainty. In: International Conference on Artificial Intelligence and Statistics. 2021. Pp. 973–981.
Jafar S.H. Financial applications of gaussian processes and Bayesian optimization. In: Bayesian Reasoning and Gaussian Processes for Machine Learning Applications. 2022. Pp. 111–122.
Kaufmann E., Cappé O., Garivier A. On Bayesian upper confidence bounds for bandit problems. Artificial Intelligence and Statistics. 2012. Vol. 22. Pp. 592–600.
Kerleguer B., Cannamela C., Garnier J. A Bayesian neural network approach to multi-fidelity surrogate modeling. International Journal for Uncertainty Quantification. 2024. Vol. 14. No. 1. Pp. 43–60.
Li J., Li M., Wu D. et al. A Bayesian networks-based risk identification approach for software process risk: The context of Chinese trustworthy software. International Journal of Information Technology & Decision Making. 2016. Vol. 15. No. 06. Pp. 1391–1412.
Lim Y.-F., Ng C.K., Vaitesswar U.S., Hippalgaonkar K. Extrapolative Bayesian optimization with Gaussian process and neural network ensemble surrogate models. Advanced Intelligent Systems. 2021. Vol. 3. No. 11. Art. 2100101.
You Z., Cartlidge J., Elliott K. et al. Improving Bayesian optimization for portfolio management with an adaptive scheduling. ArXiv Preprint. 2025. No. 2504.13529.
You Z., Cartlidge J., Elliott K. et al. Risk-aware black-box portfolio construction using Bayesian optimization with adaptive weighted Lagrangian estimator. ArXiv Preprint. 2025. No. 4. Pp. 1–15.
Keywords:
organizational systems, regularizing Bayesian approach, likelihood function, management stability, entropy, Gaussian processes, decision making.
