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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
- Griewank, 2000, Evaluating Derivatives: Principles and Techniques of Automatic Differentiation
- Cacuci, 2003, Sensitivity and Uncertainty Analysis: Theory, 1, 10.1201/9780203498798 · DOI · Paper page
- Cacuci, 2005, Sensitivity and Uncertainty Analysis: Applications to Large-Scale Systems, 2
- 10.1007/978-1-4615-6293-1 · DOI
- Glasserman, 1991, Gradient Estimation via Perturbation Analysis
- 10.1007/978-1-4615-4024-3 · DOI
- 10.1007/978-94-017-0621-6 · DOI · Paper page
- 10.1201/9781315136707 · DOI
- Rubinstein, 1993, Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method
- Saltelli, 2000, Sensitivity Analysis
- 10.1002/0470870958 · DOI · Paper page
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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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- Arriola, L. M., & Hyman, J. M. (2007). Being Sensitive to Uncertainty. Computing in Science & Engineering. https://doi.org/10.1109/mcse.2007.27
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
