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Propagation of interval and probabilistic uncertainty in cyberinfrastructure-related data processing and data fusion

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Propagation of interval and probabilistic uncertainty in cyberinfrastructure-related data processing and data fusion/ by Christian Servin, Vladik Kreinovich.
作者:	Servin, Christian.
其他作者:	Kreinovich, Vladik.
出版者:	Cham :Springer International Publishing : : 2015.,
面頁冊數:	viii, 112 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
標題:	Uncertainty (Information theory) -
電子資源:	http://dx.doi.org/10.1007/978-3-319-12628-9
ISBN:	9783319126289 (electronic bk.)

Propagation of interval and probabilistic uncertainty in cyberinfrastructure-related data processing and data fusion
Servin, Christian.

Propagation of interval and probabilistic uncertainty in cyberinfrastructure-related data processing and data fusion[electronic resource] /by Christian Servin, Vladik Kreinovich. - Cham :Springer International Publishing :2015. - viii, 112 p. :ill., digital ;24 cm. - Studies in systems, decision and control,v.152198-4182 ;. - Studies in systems, decision and control ;v. 2. .

Introduction -- Towards a More Adequate Description of Uncertainty -- Towards Justification of Heuristic Techniques for Processing Uncertainty -- Towards More Computationally Efficient Techniques for Processing Uncertainty -- Towards Better Ways of Extracting Information About Uncertainty from Data.

On various examples ranging from geosciences to environmental sciences, this book explains how to generate an adequate description of uncertainty, how to justify semiheuristic algorithms for processing uncertainty, and how to make these algorithms more computationally efficient. It explains in what sense the existing approach to uncertainty as a combination of random and systematic components is only an approximation, presents a more adequate three-component model with an additional periodic error component, and explains how uncertainty propagation techniques can be extended to this model. The book provides a justification for a practically efficient heuristic technique (based on fuzzy decision-making). It explains how the computational complexity of uncertainty processing can be reduced. The book also shows how to take into account that in real life, the information about uncertainty is often only partially known, and, on several practical examples, explains how to extract the missing information about uncertainty from the available data.

ISBN: 9783319126289 (electronic bk.)

Standard No.: 10.1007/978-3-319-12628-9doiSubjects--Topical Terms:

644010
Uncertainty (Information theory)

LC Class. No.: Q375

Dewey Class. No.: 003.54

Propagation of interval and probabilistic uncertainty in cyberinfrastructure-related data processing and data fusion
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