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Probabilistic Graphical Models with Continuous Variables for the Decision Making About Risky Episodic Behavior in the Framework of Gamma Poisson Model with Application to Public Posting Data

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Proceedings of the Sixth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’22) (IITI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 566))

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Abstract

Significant amount of accidents are related to a human factor in Socio-Cyber-Technical-Physical Systems that are used in cybersecurity or healthcare. Assessment of this factor is a part of a risk management process and it relies on an estimate of some characteristic of the behavior in interest. The data for this estimation procedure may be obtained via self–reports and interviews, that often give incomplete and imprecise information. In this paper the use of the continuous probabilistic graphical models is proposed to capture the uncertainty that accompanies the heterogeneous and incomplete behavior data and their continuity. The vine mediated Bayesian Belief network is proposed for the problem, that relies on lesser number of parameters. The data on public posting in the online social media is used to compare this approach with hybrid Bayesian Belief network that is approximated with mixtures of truncated exponentials.

The research was carried out in the framework of the project on state assignment SPC RAS No FFZF-2022-0003 and RFBR No 20-07-00839.

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Authors and Affiliations

  1. St. Petersburg Federal Research Center of the Russian Academy of Sciences (SPC RAS), 199178, St. Petersburg, Russia

    Valerie Stoliarova & Alexander Tulupyev

  2. St. Petersburg State University, 199034, St. Petersburg, Russia

    Alexander Tulupyev

Authors
  1. Valerie Stoliarova
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  2. Alexander Tulupyev
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Corresponding author

Correspondence to Valerie Stoliarova .

Editor information

Editors and Affiliations

  1. Rostov State Transport University, Rostov-on-Don, Russia

    Sergey Kovalev

  2. Rostov State Transport University, Rostov-on-Don, Russia

    Andrey Sukhanov

  3. Southern University (IMBL), Rostov-on-Don, Russia

    Imran Akperov

  4. Istinye University, Sarıyer, Istanbul, Turkey

    Sebnem Ozdemir

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Stoliarova, V., Tulupyev, A. (2023). Probabilistic Graphical Models with Continuous Variables for the Decision Making About Risky Episodic Behavior in the Framework of Gamma Poisson Model with Application to Public Posting Data. In: Kovalev, S., Sukhanov, A., Akperov, I., Ozdemir, S. (eds) Proceedings of the Sixth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’22). IITI 2022. Lecture Notes in Networks and Systems, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-031-19620-1_44

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