Computing Statistics under Interval and Fuzzy Uncertainty
Applications to Computer Science and Engineering
(Sprache: Englisch)
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...
Most algorithms for estimating such statistics assume that the sample values are...
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Klappentext zu „Computing Statistics under Interval and Fuzzy Uncertainty “
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.
Inhaltsverzeichnis zu „Computing Statistics under Interval and Fuzzy Uncertainty “
Part I Computing Statistics under Interval and Fuzzy Uncertainty: Formulation of the Problem and an Overview of General Techniques Which Can Be Used for Solving this Problem.- Part II Algorithms for Computing Statistics Under Interval and Fuzzy Uncertainty.- Part III Towards Computing Statistics under Interval and Fuzzy Uncertainty: Gauging the Quality of the Input Data.- Part IV Applications.- Part V Beyond Interval and Fuzzy Uncertainty.
Autoren-Porträt von Hung T. Nguyen, Vladik Kreinovich, Berlin Wu, Gang Xiang
Berlin Wu is a professor of Mathematical Sciences at National Chengchi University, Taipei, Taiwan. Hung T. Nguyen is a professor of Mathematical Sciences at New Mexico State University, USA.
Bibliographische Angaben
- Autoren: Hung T. Nguyen , Vladik Kreinovich , Berlin Wu , Gang Xiang
- 2014, 2012, XII, 432 Seiten, Masse: 15,5 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3642445705
- ISBN-13: 9783642445705
Sprache:
Englisch
Pressezitat
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)
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