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Learning Parameters in Directed Evidential Networks with Conditional Belief Functions

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Belief Functions: Theory and Applications (BELIEF 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8764))

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Abstract

Directed evidential networks with conditional belief functions are one of the most commonly used graphical models for analyzing complex systems and handling different types of uncertainty. A crucial step to benefit from the reasoning process in these models is to quantify them. So, we address, in this paper, the issue of estimating parameters in evidential networks from evidential databases, by applying the maximum likelihood estimation generalized to the evidence theory framework.

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Author information

Authors and Affiliations

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

    Narjes Ben Hariz & Boutheina Ben Yaghlane

Authors
  1. Narjes Ben Hariz
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  2. Boutheina Ben Yaghlane
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Editor information

Editors and Affiliations

  1. Department of Computing and Communications Technologies, Oxford Brookes University, Wheatley Campus, OX33 1HX, Oxford, UK

    Fabio Cuzzolin

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© 2014 Springer International Publishing Switzerland

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Ben Hariz, N., Ben Yaghlane, B. (2014). Learning Parameters in Directed Evidential Networks with Conditional Belief Functions. In: Cuzzolin, F. (eds) Belief Functions: Theory and Applications. BELIEF 2014. Lecture Notes in Computer Science(), vol 8764. Springer, Cham. https://doi.org/10.1007/978-3-319-11191-9_32

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  • DOI: https://doi.org/10.1007/978-3-319-11191-9_32

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11190-2

  • Online ISBN: 978-3-319-11191-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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