Uncertainty and sensitivity analysis
Overview
Uncertainty analyses
involve the propagation of uncertainty in model parameters and model structure to obtain confidence
statements for the estimate of risk and to identify the model components of dominant importance.
Uncertainty analyses are required when there is no a priori knowledge about uncertainty in the
risk estimate and when there is a chance that the failure to assess uncertainty may affect the
selection of wrong options for risk reduction.
When risk estimates are used for decision-making, sensitivity analysis allows the identification of those uncertain input parameters whose uncertainty has the greatest impact on model output uncertainty. It quantifies the relative impact of various sources of uncertainty on the output variables of interest, allowing decision-makers to assess the utility of further investment into uncertainty reduction.
This submodule is a part of the risk management module.
Learning objectives
Upon completion of this submodule, you should be able to:
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Understand different sources and representations of uncertainty
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Know how to estimate the relative impact of different sources of uncertainty on a decision
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Be able to characterize and quantify the sensitivity of different model parameters on total output uncertainty
Course material
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Introduction to sensitivity analysis |
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Python notebook on sensitivity analysis |
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Python notebook on the elementary effects method |
In these course materials, applications are presented using the NumPy and SciPy libraries for the Python programming language.
Other resources
We recommend the following sources of further information on this topic:
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The open-source OpenTURNS software platform allows sophisticated uncertainty analyses
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The open-source Dakota software platform helps analysts and decision-makers understand outcomes of predictive simulations
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