Computing Statistics under Interval and Fuzzy Uncertainty
Vladik Kreinovich Berlin Wu Hung T. Nguyen
ISBN 10: 3642445705 ISBN 13: 9783642445705
Editorial: Springer, 2014
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Descripción
Descripción:
pp. 444. N° de ref. del artículo 26127711506
Sinopsis:
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.
De la contraportada:
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.
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Detalles bibliográficos
Título: Computing Statistics under Interval and ...
Editorial: Springer
Año de publicación: 2014
Encuadernación: Encuadernación de tapa blanda
Condición: New
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COMPUTING STATISTICS UNDER INTERVAL AND FUZZY UNCERTAINTY: APPLICATIONS TO COMPUTER SCIENCE AND ENGINEERING
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Springer, Berlin, 2012
ISBN 10: 3642445705
ISBN 13: 9783642445705
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Softcover. 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. Nº de ref. del artículo: 1377680
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Computing Statistics under Interval and Fuzzy Uncertainty
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Springer Berlin Heidelberg, 2014
ISBN 10: 3642445705
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recent advances in Computing Statistics under Interval and Fuzzy Uncertainty Presents various Applications to Computer Science and Engineering In many practical situations, we are interested in statistics characterizing a population of . Nº de ref. del artículo: 5061326
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Computing Statistics under Interval and Fuzzy Uncertainty | Applications to Computer Science and Engineering
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Taschenbuch. Condición: Neu. Computing Statistics under Interval and Fuzzy Uncertainty | Applications to Computer Science and Engineering | Hung T. Nguyen (u. a.) | Taschenbuch | xii | Englisch | 2014 | Springer | EAN 9783642445705 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu Print on Demand. Nº de ref. del artículo: 105467849
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Computing Statistics under Interval and Fuzzy Uncertainty: Applications to Computer Science and Engineering (Studies in Computational Intelligence, 393)
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Computing Statistics under Interval and Fuzzy Uncertainty: Applications to Computer Science and Engineering (Studies in Computational Intelligence, 393)
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Computing Statistics under Interval and Fuzzy Uncertainty : Applications to Computer Science and Engineering
Publicado por
Springer Berlin Heidelberg, 2014
ISBN 10: 3642445705
ISBN 13: 9783642445705
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Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783642445705
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Computing Statistics under Interval and Fuzzy Uncertainty
Publicado por
Springer Berlin Heidelberg, Springer Berlin Heidelberg Jan 2014, 2014
ISBN 10: 3642445705
ISBN 13: 9783642445705
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 444 pp. Englisch. Nº de ref. del artículo: 9783642445705
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Computing Statistics under Interval and Fuzzy Uncertainty
Publicado por
Springer Berlin Heidelberg Jan 2014, 2014
ISBN 10: 3642445705
ISBN 13: 9783642445705
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 444 pp. Englisch. Nº de ref. del artículo: 9783642445705
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