Details
Propagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion
Studies in Systems, Decision and Control, Band 15
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96,29 € |
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| Verlag: | Springer |
| Format: | |
| Veröffentl.: | 20.11.2014 |
| ISBN/EAN: | 9783319126289 |
| Sprache: | englisch |
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Beschreibungen
<p>On various examples ranging from geosciences to environmental sciences, this</p><p>book explains how to generate an adequate description of uncertainty, how to justify</p><p>semiheuristic algorithms for processing uncertainty, and how to make these algorithms</p><p>more computationally efficient. It explains in what sense the existing approach to</p><p>uncertainty as a combination of random and systematic components is only an</p><p>approximation, presents a more adequate three-component model with an additional</p><p>periodic error component, and explains how uncertainty propagation techniques can</p><p>be extended to this model. The book provides a justification for a practically efficient</p><p>heuristic technique (based on fuzzy decision-making). It explains how the computational</p><p>complexity of uncertainty processing can be reduced. The book also shows how to</p><p>take into account that in real life, the information about uncertainty is often only</p><p>partially known, and, on several practical examples, explains how to extract the missing</p><p>information about uncertainty from the available data.</p><p>
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.
<p>On various examples ranging from geosciences to environmental sciences, this</p><p>book explains how to generate an adequate description of uncertainty, how to justify</p><p>semiheuristic algorithms for processing uncertainty, and how to make these algorithms</p><p>more computationally efficient. It explains in what sense the existing approach to</p><p>uncertainty as a combination of random and systematic components is only an</p><p>approximation, presents a more adequate three-component model with an additional</p><p>periodic error component, and explains how uncertainty propagation techniques can</p><p>be extended to this model. The book provides a justification for a practically efficient</p><p>heuristic technique (based on fuzzy decision-making). It explains how the computational</p><p>complexity of uncertainty processing can be reduced. The book also shows how to</p><p>take into account that in real life, the information about uncertainty is often only</p><p>partially known, and, on several practical examples, explains how to extract the missing</p><p>information about uncertainty from the available data.</p><p>
Explains how to generate an adequate description of uncertainty Shows how to justify semi-heuristic algorithms for processing uncertainty, and how to make these algorithms more computationally efficient Includes various examples and real-life cases
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