# Download Computing Statistics under Interval and Fuzzy Uncertainty: Applications to Computer Science and Engineering (Studies in Computational Intelligence) eBook

## by **Hung T. Nguyen,Vladik Kreinovich,Berlin Wu,Gang Xiang**

This book shows how to compute statistics under such interval and fuzzy uncertainty.

This book shows how to compute statistics under such interval and fuzzy uncertainty. The resulting methods are applied to computer science (optimal scheduling of different processors), to information technology (maintaining privacy), to computer engineering (design of computer chips), and to data processing in geosciences, radar imaging, and structural mechanics. The main goal is to present algorithms for computation of statistical characteristics (like variance) but under interval and fuzzy.

Электронная книга "Computing Statistics under Interval and Fuzzy Uncertainty: Applications to Computer Science and Engineering", Hung T. Nguyen, Vladik Kreinovich, Berlin Wu, Gang Xiang

Электронная книга "Computing Statistics under Interval and Fuzzy Uncertainty: Applications to Computer Science and Engineering", Hung T. Nguyen, Vladik Kreinovich, Berlin Wu, Gang Xiang. Эту книгу можно прочитать в Google Play Книгах на компьютере, а также на устройствах Android и iOS. Выделяйте текст, добавляйте закладки и делайте заметки, скачав книгу "Computing Statistics under Interval and Fuzzy Uncertainty: Applications to Computer Science and Engineering" для чтения в офлайн-режиме.

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under Interval and Fuzzy Uncertainty : Applications to Computer Science and . In many practical situations, we are interested in statistics characterizing a population of objects: .

book by Hung T. Nguyêñ. Computing Statistics under Interval and Fuzzy Uncertainty : Applications to Computer Science and Engineering. 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.

from book Computing statistics under interval and fuzzy uncertainty. Studies in Computational Intelligence

from book Computing statistics under interval and fuzzy uncertainty. Applications to computer science and engineering (p. 61-264). Studies in Computational Intelligence. Chapter · January 2012 with 15 Reads. How we measure 'reads'.

6 Abstract In many engineering applications, we have to combine probabilistic and interval uncertainty.

1 FAST ALGORITHMS FOR COMPUTING STATISTICS UNDER INTERVAL UNCERTAINTY, WITH APPLICATIONS TO COMPUTER SCIENCE AND TO ELECTRICAL AND COMPUTER ENGINEERING GANG XIANG Department of Computer Science APPROVED: Vladik Kreinovich, Chair, P. Martine Ceberio, P. Scott A. Starks, P. Dean of the Graduate School. 6 Abstract In many engineering applications, we have to combine probabilistic and interval uncertainty.

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Поиск книг BookFi BookSee - Download books for free. Computing Statistics under Interval and Fuzzy Uncertainty: Applications to Computer Science and Engineering. Hung T. Cem Kaner, Jack Falk, Hung Q. Nguyen.

Computing Statistics under Interval and Fuzzy Uncertainty, by Hung T. Nguyen, Vladik Kreinovich, Berlin . Perceptual Computing: Aiding People in Making Subjective Judgments, by Jerry M. Mendel and Dongrui Wu, IEEE Press and Wiley, 2010, ISBN 978-0-470-47876-9. Nguyen, Vladik Kreinovich, Berlin Wu, and Gang Xiang, Springer Verlag, Berlin, Heidelberg, 2012, ISBN 978-3-642-24904-4. Validated Numerics: A Short Introduction to Rigorous Computations, by Warwick Tucker, Princeton University Press, 2011, ISBN 978-0691147819.

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.

This book shows how to compute statistics under such interval and fuzzy uncertainty. The resulting methods are applied to computer science (optimal scheduling of different processors), to information technology (maintaining privacy), to computer engineering (design of computer chips), and to data processing in geosciences, radar imaging, and structural mechanics.