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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. 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.
Sensitivity analysis studies the relation between the uncertainty in a model-based the inference [clarify] and the uncertainties in the model assumptions. [ 1 ] [ 2 ] Sensitivity analysis can play an important role in epidemiology, for example in assessing the influence of the unmeasured confounding on the causal conclusions of a study. [ 3 ]
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.
Experimental uncertainty analysis. Experimental uncertainty analysis is a technique that analyses a derived quantity, based on the uncertainties in the experimentally measured quantities that are used in some form of mathematical relationship ("model") to calculate that derived quantity. The model used to convert the measurements into the ...
A sensitivity analysis method widely used to screen factors in models of large dimensionality is the design proposed by Morris. [3] The Morris method deals efficiently with models containing hundreds of input factors without relying on strict assumptions about the model, such as for instance additivity or monotonicity of the model input-output ...
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 ecology. [1]