Titlebook: Computing Statistics under Interval and Fuzzy Uncertainty; Applications to Comp Hung T. Nguyen,Vladik Kreinovich,Gang Xiang Book 20121st ed

FATAL

https://doi.org/10.1007/978-3-642-24905-1Fuzziness; Fuzzy Uncertainty; Interval Uncertainty; Soft Computing

最低点

绝种

Martin S. Olivier,Sujeet Shenointities characterizing objects from this population. For example, we are interested in the human population in a certain region, and we are interested in their heights, weights, etc..Different objects from a population have, in general, different values of the desired characteristics. Measuring, sto

温顺

Insensate

Advances in Digital Forensics IIIrst reformulate fuzzy techniques in an interval-related form..In some situations, an expert knows exactly which values of . are possible and which are not. In this situation, the expert’s knowledge can be naturally represented by describing the set of all possible values.

公社

Advances in Digital Forensics IIIsome applications, it is important to guarantee that the (unknown) actual value . of a certain quantity does not exceed a certain threshold .0. The only way to guarantee this is to have an interval . = [., . ] which is guaranteed to contain . (i.e., for which . ⊆ . ) and for which . ≤ .0.

TEN

Stephen Esposito,Gilbert Petersonis means, crudely speaking, that it is not possible to design a feasible algorithm that would compute all statistics under interval uncertainty. It is therefore necessary to restrict ourselves to statistical characteristics which are practically useful..Which statistical characteristics should we es

Intercept

Abnormal

Digital Forensics as a Surreal Narrative.In Chapter 4, we have explained that the problem of computing these values under fuzzy uncertainty can be reduced to the problem of computing the values of this characteristic under interval uncertainty. Namely, for every . ∈ [0, 1], the alpha-cut .(.) of the desired fuzzy value is the interval tha

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