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Titlebook: Computing Statistics under Interval and Fuzzy Uncertainty; Applications to Comp Hung T. Nguyen,Vladik Kreinovich,Gang Xiang Book 20121st ed

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發(fā)表于 2025-3-21 16:46:13 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書(shū)目名稱(chēng)Computing Statistics under Interval and Fuzzy Uncertainty
副標(biāo)題Applications to Comp
編輯Hung T. Nguyen,Vladik Kreinovich,Gang Xiang
視頻videohttp://file.papertrans.cn/235/234754/234754.mp4
概述Recent advances in Computing Statistics under Interval and Fuzzy Uncertainty.Presents various Applications to Computer Science and Engineering
叢書(shū)名稱(chēng)Studies in Computational Intelligence
圖書(shū)封面Titlebook: Computing Statistics under Interval and Fuzzy Uncertainty; Applications to Comp Hung T. Nguyen,Vladik Kreinovich,Gang Xiang Book 20121st ed
描述.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..
出版日期Book 20121st edition
關(guān)鍵詞Fuzziness; Fuzzy Uncertainty; Interval Uncertainty; Soft Computing
版次1
doihttps://doi.org/10.1007/978-3-642-24905-1
isbn_softcover978-3-642-44570-5
isbn_ebook978-3-642-24905-1Series ISSN 1860-949X Series E-ISSN 1860-9503
issn_series 1860-949X
copyrightSpringer-Verlag Berlin Heidelberg 2012
The information of publication is updating

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Types of Interval Data Sets: Towards Feasible Algorithmschapter shows, computing variance . under interval uncertainty is, in general, an NP-hard (computationally difficult) problem. As we will see in the following chapters, a similar problem is NP-hard for many other statistical characteristics . as well.
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Martin S. Olivier,Sujeet Shenoi corresponding to a . from the population. Based on these sample values, we need to estimate . characteristics . – i.e., characteristics that describe the population as a whole, such as the mean and the variance of different quantities, the correlation between different quantities, etc.
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Formulation of the Problem corresponding to a . from the population. Based on these sample values, we need to estimate . characteristics . – i.e., characteristics that describe the population as a whole, such as the mean and the variance of different quantities, the correlation between different quantities, etc.
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