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Comparison of Hard and Probabilistic Evidence in Bayesian Model

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Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

Abstract

Bayesian networks are powerful tools for probabilistic reasoning with uncertain evidences. Evidence originates from information based on the variables of observation. In this paper, we focus on two types of evidences: hard evidence and probabilistic evidence. We were interested in updating an evidence represented by a Bayesian model. This paper presents the application of probabilistic evidence in an adaptive user interface. Then, we compare the Bayesian model using probabilistic evidence with the Bayesian model using hard evidence.

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Author information

Authors and Affiliations

  1. ENIS, Embedded Computer System Lab, University of Sfax, Sfax, Tunisia

    Rebai Rim, Maalej Mohamed Amin, Mahfoudhi Adel & Abid Mohamed

Authors
  1. Rebai Rim
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  2. Maalej Mohamed Amin
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  3. Mahfoudhi Adel
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  4. Abid Mohamed
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Corresponding author

Correspondence to Rebai Rim .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

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Rim, R., Amin, M.M., Adel, M., Mohamed, A. (2017). Comparison of Hard and Probabilistic Evidence in Bayesian Model. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_61

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  • DOI: https://doi.org/10.1007/978-3-319-53480-0_61

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53479-4

  • Online ISBN: 978-3-319-53480-0

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