A comparison of two sampling methods for global sensitivity analysis Original Research Article

We compare the convergence properties of two different quasi-random sampling designs – Sobolʼs quasi-Monte Carlo, and Latin supercube sampling in variance-based global sensitivity analysis. We use the non-monotonic V-function of Sobolʼ as base case-study, and compare the performance of both sampling strategies at increasing sample size and dimensionality against analytical values. The results indicate that in almost all cases investigated here, the Sobolʼ design performs better. This, coupled with the fact that effective Latin supercube sampling requires a priori knowledge of the interaction properties of the function, leads us to recommend Sobolʼ sampling in most practical cases.