Combining Interval, Probabilistic, and Other Types of Uncertainty in Engineering Applications

Beschreibung

How can we solve engineering problems while taking into account data characterized by different types of measurement and estimation uncertainty: interval, probabilistic, fuzzy, etc.? This book provides a theoretical basis for arriving at such solutions, as well as case studies demonstrating how these theoretical ideas can be translated into practical applications in the geosciences, pavement engineering, etc.
In all these developments, the authors’ objectives were to provide accurate estimates of the resulting uncertainty; to offer solutions that require reasonably short computation times; to offer content that is accessible for engineers; and to be sufficiently general - so that readers can use the book for many different problems. The authors also describe how to make decisions under different types of uncertainty.
The book offers a valuable resource for all practical engineers interested in better ways of gauging uncertainty, for students eager to learn and apply the new techniques, and for researchers interested in processing heterogeneous uncertainty. 
How can we solve engineering problems while taking into account data characterized by different types of measurement and estimation uncertainty: interval, probabilistic, fuzzy, etc.? This book provides a theoretical basis for arriving at such solutions, as well as case studies demonstrating how these theoretical ideas can be translated into practical applications in the geosciences, pavement engineering, etc.
In all these developments, the authors’ objectives were to provide accurate estimates of the resulting uncertainty; to offer solutions that require reasonably short computation times; to offer content that is accessible for engineers; and to be sufficiently general - so that readers can use the book for many different problems. The authors also describe how to make decisions under different types of uncertainty.
The book offers a valuable resource for all practical engineers interested in better ways of gauging uncertainty, for students eager to learn and apply the new techniques, and for researchers interested in processing heterogeneous uncertainty. 

Presents successful methods for estimating the accuracy of the results of data processing under different models of measurement and estimation inaccuracies: probabilistic, interval, and fuzzy Offers methods that provide accurate estimates of the resulting uncertainty, do not take too much computation time, will be accessible for engineers, and are sufficiently general to cover all kinds of uncertainty Includes several illustrative case studies

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Andrew Pownuk

Themen in »Combining Interval, Probabilistic, and Other Types of Uncertainty in Engineering Applications«

Interval Uncertainty Uncertainty Probabilistic Uncertainty Fuzziness

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“The book is well structured and easy to work through. Without confusing detours, the authors always come directly to the point, clearly explaining what they are doing and why.” (Heinrich Hering, zbMATH 1432.93003, 2020)

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