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Sensitivity analysis using probability bounding
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DOI：10.1016/j.ress.2005.11.052.png)
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Abstract
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Probability bounds analysis (PBA) provides analysts a convenient means to characterize the neighborhood of possible results that would be obtained from plausible alternative inputs in probabilistic calculations. We show the relationship between PBA and the methods of interval analysis and probabilistic uncertainty analysis from which it is jointly derived, and indicate how the method can be used to assess the quality of probabilistic models such as those developed in Monte Carlo simulations for risk analyses. We also illustrate how a sensitivity analysis can be conducted within a PBA by pinching inputs to precise distributions or real values. (c) 2005 Elsevier Ltd. All rights reserved.
Keywords:
probability bounds analysis
interval analysis
second-order probability
sensitivity analysis
convolution
robust Bayes
Bayesian sensitivity analysis
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