Abstract
When only incomplete information about the probability distribution of an experiment is available, we may have to admit imprecision in the formulation of an uncertainty model. In this paper Random Set Theory is used to build possibilistic uncertainty models from sampled data. In particular Goodman's one-point coverage function of a class of random sets is estimated from data. Finally, we focus on an example to illustrate how from random sets induced possibility distributions may be used in the detection of changes in time-series data.
This work was supported by thr UK Engineering and Physical Sciences Research Council (EPSRC) under grant GR/KR09410.
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© 1997 Springer-Verlag
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Wolkenhauer, O. (1997). Qualitative uncertainty models from random set theory. In: Liu, X., Cohen, P., Berthold, M. (eds) Advances in Intelligent Data Analysis Reasoning about Data. IDA 1997. Lecture Notes in Computer Science, vol 1280. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0052875
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DOI: https://doi.org/10.1007/BFb0052875
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