Authors:
- Recent advances in Computing Statistics under Interval and Fuzzy Uncertainty
- Presents various Applications to Computer Science and Engineering
Part of the book series: Studies in Computational Intelligence (SCI, volume 393)
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Table of contents (47 chapters)
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Algorithms for Computing Statistics under Interval and Fuzzy Uncertainty
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Towards Computing Statistics under Interval and Fuzzy Uncertainty: Gauging the Quality of the Input Data
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Front Matter
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Applications
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Front Matter
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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)Authors and Affiliations
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Department of Economics, ChiangMai University, Chiangmai, Thailand
Hung T. Nguyen
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Department of Computer Science, University of Texas at El Paso, El Paso, USA
Vladik Kreinovich
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Department of Mathematical Sciences, National Chengchi University, Taipei, Taiwan
Berlin Wu
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Applied Biomathematics, Inc., Setauket, USA
Gang Xiang
Bibliographic Information
Book Title: Computing Statistics under Interval and Fuzzy Uncertainty
Book Subtitle: Applications to Computer Science and Engineering
Authors: Hung T. Nguyen, Vladik Kreinovich, Berlin Wu, Gang Xiang
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-642-24905-1
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2012
Hardcover ISBN: 978-3-642-24904-4Published: 03 November 2011
Softcover ISBN: 978-3-642-44570-5Published: 26 January 2014
eBook ISBN: 978-3-642-24905-1Published: 17 November 2011
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XII, 432
Topics: Mathematical and Computational Engineering, Artificial Intelligence, Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
Industry Sectors: Aerospace, Automotive, Biotechnology, Electronics, Energy, Utilities & Environment, Engineering, IT & Software, Oil, Gas & Geosciences, Telecommunications