Three Approaches to Sensitivity Analysis of Models

Abstract

There are three ways to implement the sensitivity analysis of quantitative models. The simplest finite-difference (FD) approach requires multiple re-runs of the forward model according to the number of model input parameters. Although this approach uses the forward model without any modifications and does not require any analytic work, its application results in a very computer-intensive algorithm, which may become a prohibitive factor in practical applications to models with a large number of input parameters. Two other approaches of sensitivity analysis—the linearization approach and adjoint approach—are substantially more computer-efficient and require just single runs of a corresponding model derived from the initial, baseline model. The general formulation and comparison of these approaches is presented in this chapter.