Variance-based sensitivity analysis

<p>Variance-based sensitivity analysis (often referred to as the Sobol’ method or Sobol’ indices, after <a href="/facts/Ilya_M._Sobol/GaxW5BCU">Ilya M. Sobol’</a>) is a form of global <a href="/facts/Sensitivity_analysis/X9x3WAnK">sensitivity analysis</a>. Working within a <a href="/facts/Probability/fgYvMhwb">probabilistic</a> framework, it decomposes the <a href="/facts/Variance/ULBJKXD1">variance</a> of the output of the model or system into fractions which can be attributed to inputs or sets of inputs. For example, given a model with two inputs and one output, one might find that 70% of the output variance is caused by the variance in the first input, 20% by the variance in the second, and 10% due to <a href="/facts/Interaction_(statistics)/Kp0Cab6w">interactions</a> between the two. These percentages are directly interpreted as measures of sensitivity. Variance-based measures of sensitivity are attractive because they measure sensitivity across the whole input space (i.e. it is a global method), they can deal with <a href="/facts/Nonlinear/4aYItALC">nonlinear</a> responses, and they can measure the effect of interactions in non-<a href="/facts/Additive_map/lyCB3yy0">additive</a> systems.
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Variance-based sensitivity analysis open-in-new

Variance-based sensitivity analysis