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Sensitivity analysis. Sensitivity analysis is the study of how the uncertainty in the output of a mathematical model or system (numerical or otherwise) can be divided and allocated to different sources of uncertainty in its inputs. [1][2] This involves estimating sensitivity indices that quantify the influence of an input or group of inputs on ...
Variance-based sensitivity analysis (often referred to as the Sobol’ method or Sobol’ indices, after Ilya M. Sobol’) is a form of global sensitivity analysis. [1][2] Working within a probabilistic framework, it decomposes the variance of the output of the model or system into fractions which can be attributed to inputs or sets of inputs ...
Sensitivity analysis can be used in epidemiology, for example in assessing the influence of the unmeasured confounding on the causal conclusions of a study. [3][5] The use of sensitivity analysis in mathematical modelling of infectious disease is suggested in [4] on the Coronavirus disease 2019 outbreak. Given the significant uncertainty at ...
Fourier amplitude sensitivity testing (FAST) is a variance-based global sensitivity analysis method. The sensitivity value is defined based on conditional variances which indicate the individual or joint effects of the uncertain inputs on the output. FAST first represents conditional variances via coefficients from the multiple Fourier series ...
A sensitivity analysis may reveal surprising insights in multi-criteria decision making (MCDM) studies aimed to select the best alternative among a number of competing alternatives. This is an important task in decision making. In such a setting each alternative is described in terms of a set of evaluative criteria.
Sensitivity analysis. Robust Bayesian analysis, also called Bayesian sensitivity analysis, investigates the robustness of answers from a Bayesian analysis to uncertainty about the precise details of the analysis. [1][2][3][4][5][6] An answer is robust if it does not depend sensitively on the assumptions and calculation inputs on which it is based.
Applications of sensitivity analysis to environmental sciences. Sensitivity analysis studies the relationship between the output of a model and its input variables or assumptions. Historically, the need for a role of sensitivity analysis in modelling, and many applications of sensitivity analysis have originated from environmental science and ...
See Corporate finance: Quantifying uncertainty. Additionally to the general motivations listed above, sensitivity analysis can help in a variety of other circumstances specific to business: To identify critical assumptions or compare alternative model structures. To guide future data collections. To optimize the tolerance of manufactured parts ...