Team:NTU-Taida/Modeling/Variance-based Sensitivity Analysis
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Revision as of 12:18, 27 September 2013
Introduction to Variance-based sensitivity analysis (VBSA)
The aim of sensitivity analysis is to examine which parameter in a model affects output most. In our model, there are 18 parameters (shown below) that have impact on GFP concentrations. The mathematical basis of variance-based sensitivity analysis is to estimate the effect of a parameter using variance. That is, how much portion of variance of GFP could the variance of a particular parameter explain?
Let X = \{x_i\}|_{i=1}^{n} be n parameters in out model, and that these parameters are in interval [0,1] by transformation. It can be assumed that output y = f(x_1, x_2, \ldots, x_n) can be decomposed as summation of several functions: