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Current Perspectives on the Application of Bayesian Networks in Different Domains

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 838))

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Abstract

Bayesian networks are powerful tools for representing relations of dependence among variables of a domain under uncertainty. Over the last decades, applications of Bayesian networks have been developed for a wide variety of subject areas, in tasks such as learning, modeling, forecasting and decision-making. Out of hundreds of related papers found, we picked a sample of 150 to study the trends of such applications over a 16-year interval. We classified the publications according to their corresponding domain of application, and then analyzed the tendency to develop Bayesian networks in determined areas of research. We found a set of indicators that help better explain these tendencies: the levels of formalization, data accuracy and data accessibility of a domain, and the level of human intervention in the primary data. The results and methodology of the current study provide insight into potential areas of research and application of Bayesian networks.

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References

  1. Alsheikh-Ali, A.A., Qureshi, W., Al-Mallah, M.H., Ioannidis, J.P.A.: Public availability of published research data in high-impact journals. PLoS ONE 6(9), e24357 (2011). https://doi.org/10.1371/journal.pone.0024357

    Article  Google Scholar 

  2. Chuang, S.H.: A resource-based perspective on knowledge management capability and competitive advantage: an empirical investigation. Expert Syst. Appl. 27(3), 459–465 (2004). https://doi.org/10.1016/j.eswa.2004.05.008

    Article  Google Scholar 

  3. Fenton, N., Neil, M., Lagnado, D.A.: A general structure for legal arguments about evidence using Bayesian networks. Cogn. Sci. 37(1), 61–102 (2012). https://doi.org/10.1111/cogs.12004

    Article  Google Scholar 

  4. Galley, M., McKeown, K., Hirschberg, J., Shriberg, E.: Identifying agreement and disagreement in conversational speech: Use of Bayesian networks to model pragmatic dependencies, pp. 669–676 (2004)

    Google Scholar 

  5. Greenspan, R.J.: Biological indeterminacy. Sci. Eng. Ethics 18(3), 447–452 (2012). https://doi.org/10.1007/s11948-012-9379-2

    Article  Google Scholar 

  6. Harzing, A., van der Wal, R.: Google scholar as a new source for citation analysis. Ethics Sci. Environ. Polit. 8, 61–73 (2008). https://doi.org/10.3354/esep00076

    Article  Google Scholar 

  7. Xu, S.J., Nourinejad, M., Lai, X., Chow, Y. J.: Network learning via multiagent inverse transportation problems. Transp. Sci. (2017). https://doi.org/10.1287/trsc.2017.0805

  8. Kasper, G., Wagner, J.: Conversation analysis in applied linguistics. Ann. Rev. Appl. Linguist. 34, 171–212 (2014). https://doi.org/10.1017/S0267190514000014

    Article  Google Scholar 

  9. Katzner, D.W.: Unmeasured Information and the Methodology of Social Scientific Inquiry, 1st edn. Kluwer Academic Publishers, Boston (2001)

    Book  Google Scholar 

  10. van Kerkhoff, L.: Integrated research: concepts of connection in environmental science and policy. Environ. Sci. Policy 8(5), 452–463 (2005). https://doi.org/10.1016/j.envsci.2005.06.002

    Article  Google Scholar 

  11. Kress, K.: Legal indeterminacy. Calif. Law Rev. 77(2), 283 (1989). https://doi.org/10.2307/3480606

    Article  Google Scholar 

  12. McCarthy, J.: Generality in artificial intelligence. Commun. ACM 30(12), 1030–1035 (1987). https://doi.org/10.1145/33447.33448

    Article  MathSciNet  MATH  Google Scholar 

  13. Mkrtchyan, L., Podofillini, L., Dang, V.: Bayesian belief networks for human reliability analysis: a review of applications and gaps (2015)

    Google Scholar 

  14. Newton, A.C.: Bayesian Belief Networks in Environmental Modelling: A Review of Recent Progress, 1st edn, pp. 13–50. Nova Science Publishers, New York (2009)

    Google Scholar 

  15. Okutan, A., Yildiz, O.T.: Software defect prediction using Bayesian networks. Empir. Softw. Eng. 19(1), 154–181 (2012). https://doi.org/10.1007/s10664-012-9218-8

    Article  Google Scholar 

  16. Pearl, J.: Probabilistic Reasoning in Intelligent Systems, 1st edn. Kaufmann, San Mateo (1988)

    MATH  Google Scholar 

  17. Prakken, H.: A logical framework for modelling legal argument, pp. 1–9. ACM Press (1993)

    Google Scholar 

  18. Rubin, A., Riznichenko, G.Y.: Mathematical Biophysics, 1st edn. Springer, Boston (2014). https://doi.org/10.1007/978-1-4614-8702-9

    Book  MATH  Google Scholar 

  19. Russell, S.J., Norvig, P., Davis, E.: Artificial Intelligence, 1st edn. Prentice Hall, Upper Saddle River (2010)

    MATH  Google Scholar 

  20. Shackley, S., Wynne, B.: Representing uncertainty in global climate change science and policy: boundary-ordering devices and authority. Sci. Technol. Hum. Values 21(3), 275–302 (1996). https://doi.org/10.1177/016224399602100302

    Article  Google Scholar 

  21. Xie, P., Li, J.H., Ou, X., Liu, P., Levy, R.: Using Bayesian networks for cyber security analysis, pp. 211–220. IEEE/IFIP (2010)

    Google Scholar 

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Acknowledgment

The research presented in this paper was supported by the RUDN University Program 5-100.

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Authors and Affiliations

  1. RUDN University, Moscow, Russia

    Galina M. Novikova & Esteban J. Azofeifa

Authors
  1. Galina M. Novikova
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  2. Esteban J. Azofeifa
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Corresponding author

Correspondence to Galina M. Novikova .

Editor information

Editors and Affiliations

  1. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Audrone Lupeikiene

  2. Information Systems Department, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Olegas Vasilecas

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

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Novikova, G.M., Azofeifa, E.J. (2018). Current Perspectives on the Application of Bayesian Networks in Different Domains. In: Lupeikiene, A., Vasilecas, O., Dzemyda, G. (eds) Databases and Information Systems. DB&IS 2018. Communications in Computer and Information Science, vol 838. Springer, Cham. https://doi.org/10.1007/978-3-319-97571-9_29

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  • DOI: https://doi.org/10.1007/978-3-319-97571-9_29

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-97570-2

  • Online ISBN: 978-3-319-97571-9

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