Possibilistic MDL: A New Possibilistic Likelihood Based Score Function for Imprecise Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10369)


Recent years have seen a surge of interest in methods for representing and reasoning with imprecise data. In this paper, we propose a new possibilistic likelihood function handling this particular form of data based on the interpretation of a possibility distribution as a contour function of a random set. The proposed function can serve as the foundation for inferring several possibilistic models. In this paper, we apply it to define a new scoring function to learn possibilistic network structure. Experimental study showing the efficiency of the proposed score is also presented.


Bayesian Network Likelihood Function Minimum Description Length Possibility Distribution Greedy Search 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Maroua Haddad
    • 1
    • 2
  • Philippe Leray
    • 2
  • Nahla Ben Amor
    • 1
  1. 1.LARODEC Laboratory ISGUniversité de TunisTunisTunisia
  2. 2.LINA-UMR CNRS 6241Université de NantesNantesFrance

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