Sensitivity analysis of model output: Variance-based methods make the difference
This paper is intended to review a number of variance-based methods used in Sensitivity Analysis (SA) to ascertain how much a model (numerical or otherwise) depends on each or some of its input parameters. A class of variance-based methods (correlation ratio or importance measure) that is capable of measuring only the main effect contribution of each input parameter on the output variance are described briefly. In addition, two methods (Sobol' and FAST) that are capable of computing the so-called Total Sensitivity Indices (TSI), which measure a parameter's main effect and all the interactions (of any order) involving that parameter, are described in details. An illustrated example demonstrates that the incorporation of total effect indices is the only way to perform a rigorous quantitative sensitivity analysis.
Bibliographic Reference: Paper presented: Winter Simulation Conference, Atlanta (US) 7-10 December, 1997
Availability: Available from (1) as Paper EN 40849 ORA
Record Number: 199711247 / Last updated on: 1997-10-10
Original language: en
Available languages: en