Derivative based global sensitivity analysis and its entropic link

TL;DR

This paper introduces a novel derivative-based upper bound for conditional entropies to improve global sensitivity analysis, especially for skewed or heavy-tailed distributions, providing a more comprehensive measure than variance-based methods.

Contribution

It presents a new entropy proxy based on derivatives that efficiently ranks uncertain variables and extends sensitivity analysis to complex distributions.

Findings

The upper bound is tight for monotonic functions.

The entropy proxy matches entropy-based indices for most tested functions.

The new proxy performs similarly to variance-based proxies in a flood model.

Abstract

Variance-based Sobol' sensitivity is one of the most well-known measures in global sensitivity analysis (GSA). However, uncertainties with certain distributions, such as highly skewed distributions or those with a heavy tail, cannot be adequately characterised using the second central moment only. Entropy-based GSA can consider the entire probability density function, but its application has been limited because it is difficult to estimate. Here we present a novel derivative-based upper bound for conditional entropies, to efficiently rank uncertain variables and to work as a proxy for entropy-based total effect indices. To overcome the non-desirable issue of negativity for differential entropies as sensitivity indices, we discuss an exponentiation of the total effect entropy and its proxy. Numerical verifications demonstrate that the upper bound is tight for monotonic functions and it provides the same input variable ranking as the entropy-based indices for about three-quarters of the 1000 random functions tested. We found that the new entropy proxy performs similarly to the variance-based proxies for a river flood physics model with 8 inputs of different distributions, and these two proxies are equivalent in the special case of linear functions with Gaussian inputs. We expect the new entropy proxy to increase the variable screening power of derivative-based GSA and to complement Sobol'-indices proxy for a more diverse type of distributions.