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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1860-949X ng.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,

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Disk Drive I/O Commands and Write Blockingir estimates are described by (imprecise, “fuzzy”) words from natural language. For example, an expert can say that the value . of the .-th quantity is approximately equal to 1.0, with an accuracy most probably of about 0.1.

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https://doi.org/10.1007/978-3-642-41148-9puting, for every possible action ., the corresponding expected utility. To be more precise, we usually know, for each action . and for each actual value of the (unknown) quantity ., the corresponding value of the utility .(.). We must use the probability distribution for . to compute the expected value e[.(.)] of this utility.

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Digital Forensics as a Surreal Narrativechapter 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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Computing Statistics under Interval and Fuzzy Uncertainty978-3-642-24905-1Series ISSN 1860-949X Series E-ISSN 1860-9503

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A Compiled Memory Analysis Toolnt . can be always computed in feasible (polynomial) time). Since we cannot always efficiently compute the upper endpoint . , we therefore need to consider cases when such an efficient computation may be possible.