Combining Interval, Probabilistic, and Other Types of Uncertainty in Engineering Applications
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 thes...
Κύριοι συγγραφείς: | , |
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Συγγραφή απο Οργανισμό/Αρχή: | |
Μορφή: | Ηλεκτρονική πηγή Ηλ. βιβλίο |
Γλώσσα: | English |
Έκδοση: |
Cham :
Springer International Publishing : Imprint: Springer,
2018.
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Έκδοση: | 1st ed. 2018. |
Σειρά: | Studies in Computational Intelligence,
773 |
Θέματα: | |
Διαθέσιμο Online: | Full Text via HEAL-Link |
Πίνακας περιεχομένων:
- Introduction
- How to Get More Accurate Estimates
- How to Speed Up Computations
- Towards a Better Understandability of Uncertainty-Estimating Algorithms
- How General Can We Go: What Is Computable and What Is Not
- Decision Making Under Uncertainty
- Conclusions.