Team:NTU-Taida/Modeling/Variance-based Sensitivity Analysis

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: