Counting data in presence of uncertainty leads to granular counts that can be represented in terms of possibility distributions. The formula of granular count is derived on the basis of two weak assumptions that can be applied in a wide variety of problems involving uncertain data. The formulation is further extended to introduce the granular sum of counts, by taking into account the interactivity of granular counts. Numerical results show the differences in terms of specificity between granular sum and a direct application of the extension principle to sum granular counts.
Possibilistic Granular Count: Derivation and Extension to Granular Sum
Mencar, Corrado
2021-01-01
Abstract
Counting data in presence of uncertainty leads to granular counts that can be represented in terms of possibility distributions. The formula of granular count is derived on the basis of two weak assumptions that can be applied in a wide variety of problems involving uncertain data. The formulation is further extended to introduce the granular sum of counts, by taking into account the interactivity of granular counts. Numerical results show the differences in terms of specificity between granular sum and a direct application of the extension principle to sum granular counts.File in questo prodotto:
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