COMPUTING STATISTICS UNDER INTERVAL AND FUZZY UNCERTAINTY: APPLICATIONS TO COMPUTER SCIENCE AND ENGINEERING

Nguyen, Hung; Kreinovich, Vladik; Wu, Berlin & Xiang, Gang

ISBN 10: 3642445705 / ISBN 13: 9783642445705

Published by Springer, Berlin, 2012

About this Item

Octavo, 430 pages. In Very good condition. Red and blue spine with white lettering. Full binding in red and blue paper. Boards show mild shelf wear and minor edge wear. Text block has light soiling on bottom edge. Note: Shelved in Netdesk Column F, ND-F. 1377680. FP New Rockville Stock. Seller Inventory # 1377680

Bibliographic Details

Title: COMPUTING STATISTICS UNDER INTERVAL AND ...

Publisher: Springer, Berlin

Publication Date: 2012

Binding: Softcover

Edition: 1st Edition

About this title

Synopsis:

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.

Review:

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)