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

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Computing Statistics under Interval and Fuzzy UncertaintyApplications to Comp

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Formulation of the Problemntities 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

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Computing under Fuzzy Uncertainty Can Be Reduced to Computing under Interval Uncertaintyrst 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.

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Computing under Interval Uncertainty: General Algorithmssome 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.

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Towards Selecting Appropriate Statistical Characteristics: The Basics of Decision Theory and the Notis 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

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