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
Keywords
Sensitivity (control systems)Computer scienceUncertainty quantificationUncertainty analysisMeasurement uncertaintyPropagation of uncertaintyData miningAlgorithmMachine learningMathematicsStatisticsSimulationEngineering
Affiliated Institutions
Related Publications
Experimentation, Validation, and Uncertainty Analysis for Engineers
Preface. 1 Experimentation, Errors, and Uncertainty. 1-1 Experimentation. 1-2 Experimental Approach. 1-3 Basic Concepts and Definitions. 1-4 Experimental Results Determined from...
An Investigation of Uncertainty and Sensitivity Analysis Techniques for Computer Models
Many different techniques have been proposed for performing uncertainty and sensitivity analyses on computer models for complex processes. The objective of the present study is ...
Consensus Filters for Sensor Networks and Distributed Sensor Fusion
Consensus algorithms for networked dynamic systems provide scalable algorithms for sensor fusion in sensor networks. This paper introduces a distributed filter that allows the n...
The Predictive Power of Diagnostic Tests and the Effect of Prevalence of Illness
There is a great interest in the potential usefulness of biomedical tests in psychiatry, but basic statistical principles required for critical evaluation of their value remain ...
Use Omega Rather than Cronbach’s Alpha for Estimating Reliability. But…
Cronbach's alpha (α) is a widely-used measure of reliability used to quantify the amount of random measurement error that exists in a sum score or average generated by a multi-i...
Publication Info
- Year
- 2007
- Type
- article
- Volume
- 9
- Issue
- 2
- Pages
- 10-20
- Citations
- 959
- Access
- Closed
External Links
Social Impact
Altmetric
PlumX Metrics
Social media, news, blog, policy document mentions
Citation Metrics
959
OpenAlex
1
Influential
24
CrossRef
Cite This
Leon Arriola,
James M. Hyman
(2007).
Being Sensitive to Uncertainty.
Computing in Science & Engineering
, 9
(2)
, 10-20.
https://doi.org/10.1109/mcse.2007.27
Identifiers
- DOI
- 10.1109/mcse.2007.27