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Approximate Inference in Directed Evidential Networks with Conditional Belief Functions Using the Monte Carlo Algorithm

  • Conference paper
Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2014)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 444))

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Abstract

Evidential networks are frameworks of interest commonly used to represent uncertainty and to reason within the belief function formalism. Despite their success in handling different uncertain situations, the exponential computational complexity which occurs when carrying out the exact inference in these networks makes the use of such models in complex problems difficult. Therefore, with real applications reaching the size of several tens or hundreds of variables, it becomes important to address the serious problem of the feasibility of the exact evidential inference. This paper investigates the issue of applying an approximate algorithm to the belief function propagation in evidential networks.

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Author information

Authors and Affiliations

  1. LARODEC Laboratory, Institut Supérieur de Gestion de Tunis, Tunisia

    Wafa Laâmari, Narjes Ben Hariz & Boutheina Ben Yaghlane

Authors
  1. Wafa Laâmari
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  2. Narjes Ben Hariz
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  3. Boutheina Ben Yaghlane
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Editor information

Editors and Affiliations

  1. University Montpellier 2, LIRMM - CNRS UMR 5506, 161, Rue Ada, 34392, Montpellier Cedex 5, France

    Anne Laurent

  2. Institute for Human Factors and Technology Management, IAT, University of Stuttgart, Nobelstraße 12, 70569, Stuttgart, Germany

    Oliver Strauss

  3. LIP6, UPMC Univ. Paris 06, CNRS UMR 7606, F-75005, Paris, France

    Bernadette Bouchon-Meunier

  4. Dept. of Information Systems, Iona College, 710 North Ave, 10801, New Rochelle, NY, USA

    Ronald R. Yager

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© 2014 Springer International Publishing Switzerland

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Laâmari, W., Ben Hariz, N., Ben Yaghlane, B. (2014). Approximate Inference in Directed Evidential Networks with Conditional Belief Functions Using the Monte Carlo Algorithm. In: Laurent, A., Strauss, O., Bouchon-Meunier, B., Yager, R.R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2014. Communications in Computer and Information Science, vol 444. Springer, Cham. https://doi.org/10.1007/978-3-319-08852-5_50

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  • DOI: https://doi.org/10.1007/978-3-319-08852-5_50

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-08851-8

  • Online ISBN: 978-3-319-08852-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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