Overview
- Presents how uncertainty-related Ideas can provide a theoretical explanation for empirical dependencies
- Explains how uncertainty can be taken into account when providing theoretical explanations
- Provides explanations of empirical dependencies in different application areas such as decision making, electrical engineering, image processing, logic pedagogy, psychology, and transportation engineering
Part of the book series: Studies in Systems, Decision and Control (SSDC, volume 306)
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Table of contents (20 chapters)
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Front Matter
Keywords
About this book
This book shows how to provide uncertainty-related theoretical justification for empirical dependencies, on the examples from numerous application areas. Such justifications are needed, since without them, practitioners may be reluctant to use these dependencies: purely empirical formulas often turn out to hold only in some cases.
Examples of new theoretical explanations range from fundamental physics (quark confinement, galaxy superclusters, etc.) and geophysics (earthquake analysis) to transportation and electrical engineering to computer science (image processing, quantum computing) and pedagogy (equity, effect of repetitions). The book is useful to students and specialists in the corresponding areas.
Most of the examples use common general techniques, so the book is also useful to practitioners and researchers in other application areas who look for ways to provide theoretical justifications for their areas’ empirical dependencies.
Editors and Affiliations
Bibliographic Information
Book Title: How Uncertainty-Related Ideas Can Provide Theoretical Explanation For Empirical Dependencies
Editors: Martine Ceberio, Vladik Kreinovich
Series Title: Studies in Systems, Decision and Control
DOI: https://doi.org/10.1007/978-3-030-65324-8
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-65323-1Published: 21 March 2021
Softcover ISBN: 978-3-030-65326-2Published: 21 March 2022
eBook ISBN: 978-3-030-65324-8Published: 20 March 2021
Series ISSN: 2198-4182
Series E-ISSN: 2198-4190
Edition Number: 1
Number of Pages: X, 151
Number of Illustrations: 2 b/w illustrations
Topics: Computational Intelligence, Mathematical and Computational Engineering