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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 the output.
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 has important applications in model calibration. One application of sensitivity analysis addresses the question of "What's important to model or system development?" One can seek to identify important connections between observations, model inputs, and predictions or forecasts.
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 ...
This analysis excluded participants with NYHA 4, ... However, researchers used a specific method in their sensitivity analysis to try to account for this. While researchers were able to account ...
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 .
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 ]
The rate sensitivity of Brazil's debt is poised to accelerate as the country's central bank aggressively tightens money supply to combat inflation, overshadowing improvements in the primary budget ...