Abstract
In this chapter we address once more the common treatment of imprecision and uncertainty. Our approach relies on the attachment of “masses” to vague data. Of course there are extensive similarities with the concept of belief functions [Shafer 1976] and even more with the transferable mass model [Smets 1978], but an important difference remains: we conceive mass distributions (in the case of imprecise data) just as a condensed representation of weighted (random) sets, where sets are used for the representation of imprecise data. Note that this does not necessarily require us to follow the purely frequentistic interpretation of uncertainty presented above, but allows also a “subjective” view. The practical value of the methods we develop in the sequel arises from the fact that they can be applied even if the underlying random set is unknown.
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© 1991 Springer-Verlag Berlin Heidelberg
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Kruse, R., Schwecke, E., Heinsohn, J. (1991). Mass Distributions. In: Uncertainty and Vagueness in Knowledge Based Systems. Artificial Intelligence. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-76702-9_6
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DOI: https://doi.org/10.1007/978-3-642-76702-9_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-76704-3
Online ISBN: 978-3-642-76702-9
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