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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Computing Statistics Under Interval and Fuzzy Uncertainty: Applications to Computer Science and Engineering

Computing Statistics Under Interval and Fuzzy Uncertainty: Applications to Computer Science and Engineering

Kreinovich, Vladik
Nguyen, Hung T.
Wu, Berlin

Hardcover

Series: Studies in Computational Intelligence, Book 393

General ComputersGeneral MathematicsProbability & Statistics

ISBN10: 3642249043
ISBN13: 9783642249044
Publisher: Springer
Published: Nov 3 2011
Pages: 432
Weight: 1.75
Height: 1.00 Width: 6.14 Depth: 9.21
Language: English

In many practical situations, we are interested in statistics characterizing a population of objects: e.g. in the mean height of people from a certain area.

Most algorithms for estimating such statistics assume that the sample values are exact. In practice, sample values come from measurements, and measurements are never absolutely accurate. Sometimes, we know the exact probability distribution of the measurement inaccuracy, but often, we only know the upper bound on this inaccuracy. In this case, we have interval uncertainty: e.g. if the measured value is 1.0, and inaccuracy is bounded by 0.1, then the actual (unknown) value of the quantity can be anywhere between 1.0 - 0.1 = 0.9 and 1.0 ] 0.1 = 1.1. In other cases, the values are expert estimates, and we only have fuzzy information about the estimation inaccuracy.

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