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Being Sensitive to Uncertainty
Abstract
Predictive modeling's effectiveness is hindered by inherent uncertainties in the input parameters. Sensitivity and uncertainty analysis quantify these uncertainties and identify the relationships between input and output variations, leading to the construction of a more accurate model. This survey introduces the application, implementation, and underlying principles of sensitivity and uncertainty quantification
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Research works printed but not linked: 11
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Metadata
- Authors
- Arriola, L. M., & Hyman, J. M.
- Venue
- Computing in Science & Engineering
- Published
- 2007-03
- Volume
- 9
- Issue
- 2
- Pages
- 10-20
- ISSN
- 1521-9615 (ISSN-L)
- 1558-366X
- OpenAlex
- W2083551778
- Cited by
- 953
- References
- 0
- FWCI
- 8.47
- Type
- journal article
- Language
- en
- Open access
- closed
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