Propagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion: 15 (Studies in Systems, Decision and Control, 15) - Softcover

Servin, Christian; Kreinovich, Vladik

Softcover

ISBN 10: 3319385879 ISBN 13: 9783319385877

Publisher: Springer, 2016

Synopsis

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.

About this title

From the Back Cover

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.

Edition details

• Publisher: Springer
• Publication date: 2016
• Language: English
• ISBN 10: 3319385879
• ISBN 13: 9783319385877
• Binding: Paperback
• Number of pages: 120