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International Conference on Scalable Uncertainty Management

SUM 2016: Scalable Uncertainty Management pp 33–48Cite as

Possibilistic Graphical Models for Uncertainty Modeling

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9858))

Abstract

Belief graphical models, especially the probabilistic ones, have now a long history and they are successfully used in a wide range of tasks and applications. Thanks to independence relations, they allow a compact representation of complex and uncertain information and they greatly simplify the critical tasks of information elicitation, representation and inference. Many alternative belief graphical models have been proposed to overcome the limits of probability theory and take advantage of the decomposability property. This paper surveys most of the works dealing with belief graphical models based on possibility theory, an alternative uncertainty theory particularly suited for dealing with incomplete and qualitative uncertain knowledge.

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Author information

Authors and Affiliations

  1. University of Lille Nord de France, 59000, Lille, France

    Karim Tabia

  2. CRIL University of Artois & CNRS, 62300, Lens, France

    Karim Tabia

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  1. Karim Tabia
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Correspondence to Karim Tabia .

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Editors and Affiliations

  1. Cardiff University , Cardiff, United Kingdom

    Steven Schockaert

  2. Télécom ParisTech , Paris, Paris, France

    Pierre Senellart

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Tabia, K. (2016). Possibilistic Graphical Models for Uncertainty Modeling. In: Schockaert, S., Senellart, P. (eds) Scalable Uncertainty Management. SUM 2016. Lecture Notes in Computer Science(), vol 9858. Springer, Cham. https://doi.org/10.1007/978-3-319-45856-4_3

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  • DOI: https://doi.org/10.1007/978-3-319-45856-4_3

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