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Directed Evidential Networks with Conditional Belief Functions

  • Conference paper
Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU 2003)

Abstract

The main question addressed in this paper is how to represent belief functions independencies by graphical model. Directed evidential networks (DEVNs) with conditional belief functions are then proposed. These networks are directed acyclic graphs (DAGs) similar to Bayesian networks but instead of using probability functions, we use belief functions. Directed evidential network with conditional belief functions has the advantage of providing an appropriate representation of the knowledge that can be produced as conditional relationships.

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© 2003 Springer-Verlag Berlin Heidelberg

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Yaghlane, B.B., Smets, P., Mellouli, K. (2003). Directed Evidential Networks with Conditional Belief Functions. In: Nielsen, T.D., Zhang, N.L. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2003. Lecture Notes in Computer Science(), vol 2711. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-45062-7_24

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  • DOI: https://doi.org/10.1007/978-3-540-45062-7_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40494-1

  • Online ISBN: 978-3-540-45062-7

  • eBook Packages: Springer Book Archive

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