Skip to main content
Springer Nature Link
Log in

Dynamic Bayesian Network Model of a Country’s Economic Extension

  • Conference paper
  • First Online:
Advances in Intelligent Systems and Computing V (CSIT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1293))

Included in the following conference series:

  • 452 Accesses

Abstract

This paper presents the studies’ results on the probability-determined models development based on Bayesian networks to estimate the economic development measure of Ukraine. Considering that one of the difficulties in the Bayesian networks development is the exponential increase in the parameters amount in conditional probability tables (CPT), this study proposes a technique for applying Noisy-MAX nodes to model economic processes taking into account the time component. It is shown that if the proportion of enterprises that implement innovations is increased now by 31%, while the share of profits of these enterprises increases by only 2%, at the next time step the measure of manufacturability and innovation of Ukraine will rise by 81% and will tend to the maximum.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
CHF34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
CHF 24.95
Price includes VAT (Switzerland)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
CHF 141.50
Price excludes VAT (Switzerland)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
CHF 177.00
Price excludes VAT (Switzerland)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Naukova ta innovatsiina diialnist v Ukraini u 2012–2017 rokakh. [Statystychnyi zbirnyk], p. 289. KDP (2017). (in Ukrainian)

    Google Scholar 

  2. Greenwald, A.G., Schute, P.C., Shiveley, J.L.: Brachial plexus birth palsy: a 10-year report on the incidence and prognosis. Pediatr. Orthop. 4, 689–692 (1984)

    Article  Google Scholar 

  3. Froot, K.A., Stein, J.C.: Exchange rates and foreign direct investment: an imperfect capital markets approach. Q. J. Econ. 106(4), 1191–1217 (1991)

    Article  Google Scholar 

  4. Koller, D.A.: General algorithm for approximate inference and its application to hybrid Bayes Nets. In: Proceedings of the 15th UAI, pp. 324–333 (1999)

    Google Scholar 

  5. Heskes, T.: Generalized belief propagation for approximate inference in hybrid Bayesian networks. In: Proceedings of the 9th International Workshop Artificial Intelligence and Statistics (AISTATS), pp. 30–37 (2003)

    Google Scholar 

  6. Kuznyetsova, N.: Integrated approach to credit rating. In: International Scientific Conference on “Intelligence, Integrity, Reliability”, pp. 30–31. NTUU KPI, Kiev (2010)

    Google Scholar 

  7. Lytvynenko, V., Savina, N., Pyrtko, M., Voronenko, M., Baranenko, R., Lopushynskyi, I.: Development, validation and testing of the Bayesian network to evaluate the national law enforcement agencies’ work. In: Proceedings of the 9th International Conference on Advanced Computer Information Technologies, ACIT 2019, pp. 252–256 (2019). ISBN 978-1-7281-0449-2

    Google Scholar 

  8. Ding, J., Kramer, B., Bai, Y., Chen, H.: Backward inference in Bayesian networks for distributed systems management. J. Netw. Syst. Manag. 13(4), 409–427 (2005)

    Article  Google Scholar 

  9. Plach, M.: Bayesian networks as models of human judgement under uncertainty: the role of causal assumptions in belief updating. Kognitionswissenschaft 8(1), 30–39 (1999)

    Article  Google Scholar 

  10. Voronenko, M., Lurie, I., Boskin, O., Zhunissova, U., Baranenko, R., Lytvynenko, V.: Using Bayesian methods for predicting the development of children autism. In: IEEE International Conference on Advanced Trends in Information Theory, ATIT 2019, Kyiv, Ukraine, pp. 525–529 (2019). https://doi.org/10.1109/atit49449.2019.9030523

  11. Cobb, B.R., Shenoy, P.P.: A Comparison of Bayesian and Belief Function Reasoning. Working Paper No. 292, University of Kansas School of Business (2002). http://citeseer.ist.psu.edu/cobb02comparison.html

  12. Chickering, D., Heckerman, D.: Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables. Mach. Learn. 29, 181–212 (1997)

    Article  Google Scholar 

  13. Tahboub, K.A.: Intelligent human-machine interaction based on dynamic Bayesian networks probabilistic intention recognition. J. Intell. Robot Syst. 45(1), 31–52 (2006)

    Article  Google Scholar 

  14. Agostinelli, C., Rotondi, R.: Using Bayesian belief networks to analyze the stochastic dependence between interevent time and size of earthquakes. J. Seismolog. 7, 281–299 (2003)

    Article  Google Scholar 

  15. Lytvynenko, V., Savina, N., Krejci, J., Fefelov, A., Lurie, I., Voronenko, M., Lopushynskyi, I., Vorona, P.: Dynamic bayesian networks in the problem of localizing the narcotic substances distribution. AISC, vol. 1080, pp. 421–438. Springer (2019)

    Google Scholar 

  16. Li, J., Aickelin, U.: A Bayesian optimization algorithm for the nurse scheduling problem. In: Proceedings of the Congress on Evolutionary Computation, Canberra, Australia, pp. 2149–2156 (2003)

    Google Scholar 

  17. Hulst, I.R.J.: Modeling physiological processes with dynamic Bayesian networks. Thesis Paper. University of Pittsburgh (2006)

    Google Scholar 

  18. Bonafede, C.E., Giudici, P.: Bayesian networks for enterprise risk assessment. http://arxiv.org/PS_cache/physics/pdf/0607/0607226v1.pdf

  19. Smaili, C., Najjar, M.E., Charpillet, F.: Multi-sensor fusion method using dynamic Bayesian network for precise vehicle localization and road matching. In: ICTAI, vol. 1, pp. 146–151 (2007)

    Google Scholar 

  20. Bidyuk, P., Gozhyj, A., Kalinina, I.: Probabilistic inference based on LS-method modifications in decision making problems. In: Lecture Notes in Computational Intelligence and Decision Making. Advances in Intelligent Systems and Computing, vol. 1020. pp 422–433. Springer (2019). https://doi.org/10.1007/978-3-030-26474-1_30

  21. Acid, S., Campos, L.M., Castellano, J.: Learning Bayesian network classifiers: searching in a space of partially directed acyclic graphs. Mach. Learn. 59, 213–235 (2005). https://doi.org/10.1007/s10994-005-0473-4

  22. Friedman, N., Koller, D.: Being Bayesian about network structure. A Bayesian approach to structure discovery in Bayesian networks. Mach. Learn. 50, 95–125 (2003)

    Article  Google Scholar 

  23. Eskandari, F., Meshkani, R.M.: Empirical Bayes analysis of log-linear models for a generalized finite stationary Markov chain. Metrika 59, 173–191 (2004)

    Article  MathSciNet  Google Scholar 

  24. Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, p. 550. Morgan Kauffmann Publishers, Inc. (1988)

    Google Scholar 

  25. Díez, F.J., Galán, S.F.: Efficient computation for the noisy MAX. Int. J. Int. Syst. 18(2), 165–177 (2004)

    Article  Google Scholar 

  26. Koller, D., Pfeffer, A.: Learning probabilities for noisy first-order rules. In: Proceedings of the International Joint Conference on Artificial Intelligence, pp. 1316–1321 (1997)

    Google Scholar 

  27. Maumy, M., Boulanger, B., Dewe, W., Gilbert, A., Govaerts, B.: Risk management for analytical methods: conciliating objectives of methods, validation phase and routine decision rules, p. 4 (2007)

    Google Scholar 

  28. Dean, T., Kanazawa, K.: Probabilistic temporal reasoning. In: Proceedings of the National Conference on Artificial Intelligence, AAAI – 1988, pp. 525–529. AAAI Press/The MIT Press (1988)

    Google Scholar 

  29. Murphy, K.P.: Dynamic Bayesian networks: representation, inference and learning. Thesis Paper. University of California, Berkeley (2002)

    Google Scholar 

  30. De Campos, L., Castellano, J.: Bayesian network learning algorithms using structural retrictions. Int. J. Approx. Reason. 45(2), 233–254 (2007). https://doi.org/10.1016/j.ijar.2006.06.009

  31. Heckerman, D., Geiger, D., Chickering, D.M.: Learning Bayesian networks: the combination of knowledge and statistical data. Mach. Learn. 20(3), 197–243 (1995). https://doi.org/10.1023/a:1022623210503

  32. Lytvynenko, V., Savina, N., Voronenko, M., Doroschuk, N., Smailova, S., Boskin, O., Kravchenko, T.: Development, validation and testing of the Bayesian network of educational institutions financing. In: The Crossing Point of Intelligent Data Acquisition & Advanced Computing Systems and East & West Scientists, IDAACS 2019 (2019). https://doi.org/10.1109/IDAACS.2019.8924307

  33. Romanko, O., Voronenko, M., Savina, N., Zhorova, I., Wójcik, W., Lytvynenko, V.: The use of static Bayesian networks for situational modeling of national economy competitiveness. In: IEEE International Conference on Advanced Trends in Information Theory, ATIT 2019, Kyiv, Ukraine, pp. 501–505 (2019). https://doi.org/10.1109/atit49449.2019.9030515

Download references

Author information

Authors and Affiliations

  1. Kherson National Technical University, Kherson, Ukraine

    Mariia Voronenko, Nataliia Krugla & Volodymyr Lytvynenko

  2. National University of Water and Environmental Engineering, Rivne, Ukraine

    Dmytro Nikytenko & Nataliia Savina

  3. Jan Evangelista Purkyne University in Usti nad Labem, Usti nad Labem, Czech Republic

    Jan Krejci

  4. University of State Fiscal Service of Ukraine, Kyiv, Ukraine

    Oleksandr Naumov

Authors
  1. Mariia Voronenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dmytro Nikytenko
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jan Krejci
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nataliia Krugla
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Oleksandr Naumov
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Nataliia Savina
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Volodymyr Lytvynenko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mariia Voronenko .

Editor information

Editors and Affiliations

  1. Department of Artificial Intelligence, Lviv Polytechnic National University, Lviv, Ukraine

    Natalya Shakhovska

  2. Institute of Computer Science and Information Technologies, Lviv Polytechnic National University, Lviv, Ukraine

    Mykola O. Medykovskyy

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Voronenko, M. et al. (2021). Dynamic Bayesian Network Model of a Country’s Economic Extension. In: Shakhovska, N., Medykovskyy, M.O. (eds) Advances in Intelligent Systems and Computing V. CSIT 2020. Advances in Intelligent Systems and Computing, vol 1293. Springer, Cham. https://doi.org/10.1007/978-3-030-63270-0_10

Download citation

Publish with us

Policies and ethics

Search

Navigation

  NODES
innovation 2
INTERN 6
Note 1
Project 1