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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.
In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true ...
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 ]
Sensitivity first definition: the ratio between output and input signal, or the slope of the output versus input response curve of a transducer, microphone or sensor; Sensitivity second definition: the minimum magnitude of input signal required to produce an output signal with a specified signal-to-noise ratio of an instrument or sensor
EE is applied to identify non-influential inputs for a computationally costly mathematical model or for a model with a large number of inputs, where the costs of estimating other sensitivity analysis measures such as the variance-based measures is not affordable. Like all screening, the EE method provides qualitative sensitivity analysis ...
Applications of sensitivity analysis in epidemiology; Applications of sensitivity analysis to environmental sciences; Applications of sensitivity analysis to model calibration; Applications of sensitivity analysis to multi-criteria decision making; Sensitivity analysis; Variance-based sensitivity analysis
A sensitivity guarantees that the distance from the critical point to the Nyquist curve is always greater than and the Nyquist curve of the loop transfer function is always outside a circle around the critical point + with the radius , known as the sensitivity circle.