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Studies in Computational Intelligence

Computing Statistics under Interval and Fuzzy Uncertainty

Applications to Computer Science and Engineering

Authors: Nguyen, H.T., Kreinovich, V., Wu, B., Xiang, G.

  • Recent advances in Computing Statistics under Interval and Fuzzy Uncertainty
  • Presents various Applications to Computer Science and Engineering
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About this book

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.

Reviews

From the reviews:

“This book is a research exposition by Kreinovich and coworkers. … The main goal is to present algorithms for computation of statistical characteristics (like variance) but under interval and fuzzy uncertainty of the available data. In this book, fuzzy uncertainty is reduced to interval uncertainty by alpha-cutwise consideration of (convex) fuzzy uncertainty. … For increase of readability, mathematical proofs are presented always at the end of the chapters.” (Wolfgang Näther, Zentralblatt MATH, Vol. 1238, 2012)

Table of contents (44 chapters)

  • Formulation of the Problem

    Nguyen, Hung T. (et al.)

    Pages 3-8

  • Computing Statistics under Probabilistic and Interval Uncertainty: A Brief Description

    Nguyen, Hung T. (et al.)

    Pages 9-10

  • Computing Statistics under Fuzzy Uncertainty: Formulation of the Problem

    Nguyen, Hung T. (et al.)

    Pages 11-17

  • Computing under Fuzzy Uncertainty Can Be Reduced to Computing under Interval Uncertainty

    Nguyen, Hung T. (et al.)

    Pages 19-24

  • Computing under Interval Uncertainty: Traditional Approach Based on Uniform Distributions

    Nguyen, Hung T. (et al.)

    Pages 25-27

Buy this book

eBook $149.00
price for USA in USD (gross)
  • ISBN 978-3-642-24905-1
  • Digitally watermarked, DRM-free
  • Included format: PDF
  • ebooks can be used on all reading devices
  • Immediate eBook download after purchase
Hardcover $249.99
price for USA in USD
  • ISBN 978-3-642-24904-4
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Softcover $199.99
price for USA in USD
  • ISBN 978-3-642-44570-5
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.
Rent the eBook  
  • Rental duration: 1 or 6 month
  • low-cost access
  • online reader with highlighting and note-making option
  • can be used across all devices
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Bibliographic Information

Bibliographic Information
Book Title
Computing Statistics under Interval and Fuzzy Uncertainty
Book Subtitle
Applications to Computer Science and Engineering
Authors
Series Title
Studies in Computational Intelligence
Series Volume
393
Copyright
2012
Publisher
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer-Verlag Berlin Heidelberg
eBook ISBN
978-3-642-24905-1
DOI
10.1007/978-3-642-24905-1
Hardcover ISBN
978-3-642-24904-4
Softcover ISBN
978-3-642-44570-5
Series ISSN
1860-949X
Edition Number
1
Number of Pages
XII, 432
Topics