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Maximum Likelihood Estimation of the Dead Time Period Duration in the Modulated Semi-synchronous Generalized Flow of Events

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Information Technologies and Mathematical Modelling - Queueing Theory and Applications (ITMM 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 638))

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Abstract

This paper is focused on studying the modulated semi-synchronous generalized flow of events which is one of the mathematical models for incoming streams of events (claims) in computer communication networks and is related to the class of doubly stochastic Poisson processes (DSPPs). The flow is considered in conditions of its incomplete observability, when the dead time period of a constant duration T is generated after every registered event. This paper is devoted to the maximum likelihood estimation of the dead time period duration on monitoring the time moments of the flow events occurrence.

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Acknowledgments

The work is supported by Tomsk State University Competitiveness Improvement Program.

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Authors and Affiliations

  1. Department of Operations Research, Faculty of Applied Mathematics and Cybernetics, National Research Tomsk State University, 36 Lenina Avenue, Tomsk, 634050, Russia

    Maria Bakholdina & Alexander Gortsev

Authors
  1. Maria Bakholdina
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  2. Alexander Gortsev
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Corresponding author

Correspondence to Maria Bakholdina .

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Editors and Affiliations

  1. Belarusian State University , Minsk, Belarus

    Alexander Dudin

  2. Tomsk State University , Tomsk, Russia

    Alexander Gortsev

  3. Tomsk State University , Tomsk, Russia

    Anatoly Nazarov

  4. Kemerovo State University , Anzhero-Sudzhensk, Russia

    Rafael Yakupov

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Bakholdina, M., Gortsev, A. (2016). Maximum Likelihood Estimation of the Dead Time Period Duration in the Modulated Semi-synchronous Generalized Flow of Events. In: Dudin, A., Gortsev, A., Nazarov, A., Yakupov, R. (eds) Information Technologies and Mathematical Modelling - Queueing Theory and Applications. ITMM 2016. Communications in Computer and Information Science, vol 638. Springer, Cham. https://doi.org/10.1007/978-3-319-44615-8_1

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  • DOI: https://doi.org/10.1007/978-3-319-44615-8_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-44614-1

  • Online ISBN: 978-3-319-44615-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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