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Titlebook: Measuring Uncertainty within the Theory of Evidence; Simona Salicone,Marco Prioli Book 2018 Springer International Publishing AG, part of

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發(fā)表于 2025-3-21 16:16:37 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Measuring Uncertainty within the Theory of Evidence
編輯Simona Salicone,Marco Prioli
視頻videohttp://file.papertrans.cn/629/628227/628227.mp4
概述Defines a rigorous mathematical setting that fosters the identification of an effective uncertainty propagation method.Offers a beneficial alternative approach using examples of uncertainty propagatio
叢書名稱Springer Series in Measurement Science and Technology
圖書封面Titlebook: Measuring Uncertainty within the Theory of Evidence;  Simona Salicone,Marco Prioli Book 2018 Springer International Publishing AG, part of
描述This monograph considers the evaluation and expression of measurement uncertainty within the mathematical framework of the Theory of Evidence. With a new perspective on the metrology science, the text paves the way for innovative applications in a wide range of areas. Building on Simona Salicone’s .Measurement Uncertainty: An Approach via the Mathematical Theory of Evidence., the material covers further developments of the Random Fuzzy Variable (RFV) approach to uncertainty and provides a more robust mathematical and metrological background to the combination of measurement results that leads to a more effective RFV combination method..While the first part of the book introduces measurement uncertainty, the Theory of Evidence, and fuzzy sets, the following parts bring together these concepts and derive an effective methodology for the evaluation and expression of measurement uncertainty. A supplementary downloadable program allows the readers tointeract with the proposed approach by generating and combining RFVs through custom measurement functions. With numerous examples of applications, this book provides a comprehensive treatment of the RFV approach to uncertainty that is suitab
出版日期Book 2018
關(guān)鍵詞Evidence-Based Probability; Joint Possibility distributions; Measurement Uncertainty; Probability Theor
版次1
doihttps://doi.org/10.1007/978-3-319-74139-0
isbn_softcover978-3-030-08924-5
isbn_ebook978-3-319-74139-0Series ISSN 2198-7807 Series E-ISSN 2198-7815
issn_series 2198-7807
copyrightSpringer International Publishing AG, part of Springer Nature 2018
The information of publication is updating

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發(fā)表于 2025-3-21 20:13:57 | 只看該作者
Simona Salicone,Marco PrioliDefines a rigorous mathematical setting that fosters the identification of an effective uncertainty propagation method.Offers a beneficial alternative approach using examples of uncertainty propagatio
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發(fā)表于 2025-3-22 01:46:13 | 只看該作者
Springer Series in Measurement Science and Technologyhttp://image.papertrans.cn/m/image/628227.jpg
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Introduction,It is widely recognized, by the scientific and technical community, that measurements are the bridge between the empiric world and that of the abstract concepts. In fact, from a quantitative point of view, measurements represent the only possible source of knowledge in the description of a particular feature or phenomenon of the physical world.
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Mathematical Methods to Handle Measurement UncertaintyAs stated in Chapter ., the modern Theory of Measurements is based on the Theory of Uncertainty that has replaced the old Theory of Errors.
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發(fā)表于 2025-3-22 18:29:41 | 只看該作者
Introduction: Probability and Belief FunctionsThe mathematical Theory of Evidence has been introduced by Shafer in the 1970s as a reinterpretation of Dempster’s statistical inference. Shafer’s Theory of Evidence begins with the familiar idea of using a number between 0 and 1 to indicate the degree of belief for a proposition on the basis of the available evidence.
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Particular Cases of the Theory of EvidenceMost of the basic definitions given in the previous sections depend on the focal elements. Therefore, adding suitable constraints to the focal elements may lead to some interesting particular cases of the Theory of Evidence.
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