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Communication Dans Un Congrès Année : 2005

Representation of Incomplete Probabilistic Information

Résumé

This article deals with the compact representation of incomplete probabilistic knowledge which can be encountered in risk evaluation problems, for instance in environmental studies. Various kinds of knowledge are considered such as expert opinions about characteristics of distributions or poor statistical information. Our approach is based on probability families encoded by possibility distributions and belief functions. In each case, a technique for representing the available imprecise probabilistic information faithfully is proposed, using different uncertainty frameworks (possibility theory, probability theory, belief functions...). Moreover the use of probability-possibility transformations enables confidence intervals to be encompassed by cuts of possibility distributions, thus making the representation stronger. The respective appropriateness of pairs of cumulative distributions, continuous possibility distributions or discrete random sets for representing information about the mean value, the mode, the median and other fractiles of ill-known probability distributions is discussed in detail.
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Dates et versions

hal-03451878 , version 1 (23-01-2023)

Identifiants

  • HAL Id : hal-03451878 , version 1

Citer

Cédric Baudrit, Didier Dubois, Hélène Fargier. Representation of Incomplete Probabilistic Information. 2nd International Conference on Soft Methodology and Random Information Systems (SMPS 2004), Sep 2004, Oviedo, Spain. pp.149-156. ⟨hal-03451878⟩
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