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Measuring Uncertainty for Interval Belief Structures and its Application for Analyzing Weather Forecasts

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Advances in Fuzzy Logic and Technology 2017 (EUSFLAT 2017, IWIFSGN 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 641))

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Abstract

While analyzing statistical data we face with a problem of modeling uncertainty. One among well justified models is based on belief structures that allow us to describe imprecision and conflict in information. We use this model for analyzing contradiction in weather forecasts. For this aim we build several measures of contradiction based on the introduced imprecision index and the disjunctive aggregation rule for interval belief structures. We use these characteristics for analyzing weather forecasts.

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Notes

  1. 1.

    Smets considers also in [17] interval belief structures, but they are not finitely defined.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Myasnitskaya 20, 101000, Moscow, Russia

    Andrey G. Bronevich & Natalia S. Spiridenkova

Authors
  1. Andrey G. Bronevich
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  2. Natalia S. Spiridenkova
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Corresponding author

Correspondence to Andrey G. Bronevich .

Editor information

Editors and Affiliations

  1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Eulalia Szmidt

  3. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Sławomir Zadrożny

  4. Department of Bioinformatics and Mathematical Modelling, Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Krassimir T. Atanassov

  5. WIT - Warsaw School of Information Technology, Warsaw, Poland

    Maciej Krawczak

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Bronevich, A.G., Spiridenkova, N.S. (2018). Measuring Uncertainty for Interval Belief Structures and its Application for Analyzing Weather Forecasts. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 641. Springer, Cham. https://doi.org/10.1007/978-3-319-66830-7_25

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  • DOI: https://doi.org/10.1007/978-3-319-66830-7_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66829-1

  • Online ISBN: 978-3-319-66830-7

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