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Key steps of the methodology consist of assessing static load effect, dynamic amplification, model uncertainty, sensitivity factors and target reliability.
As a model of an interested party's preference ranking of the motor vehicles, the subsequent uncertainty (sensitivity) analysis considered here, relates to what minimal (lean) changes would be necessary to a vehicle's emissions so that their preference ranking is improved.
While the approach of using a detailed chemistry-transport model is feasible for specific policy assessments, computational requirements preclude the assessment of a wide range of policy options, quantification of uncertainty, sensitivity studies, or a timely response to policy questions.
Uncertainty, sensitivity analysis, and plausibility of the assumptions should always be presented and discussed.
To handle this type of uncertainty, sensitivity analysis is usually employed [ 23, 24, 43].
In order to account for uncertainty, sensitivity analysis was performed by estimating a frame of trauma follow-up costs, based on the two different scenario types.
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In order to address the model "relevance trilemma" – balancing complexity, uncertainty and sensitivity, a global uncertainty and sensitivity analysis (GUSA) was conducted on these five levels of algorithm complexity.
It is then necessary to do the uncertainty and sensitivity analysis such that the sensitive parameters can be detected.
Through a deep scientific literature investigation, several applications were indentified, described and discussed, including the role of uncertainty and sensitivity analyses on parameter screening, reliability analysis, robustness design, decision making process, meta-model construction and embedded uncertainty and sensitivity.
(See Methods section for description of uncertainty and sensitivity analyses).
(See the Methods section below for a description of the uncertainty and sensitivity analyses).
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