UC Irvine

Variance-based analysis has emerged as method of choice for quantifying the sensitivity of the output, y, of a scalar-valued square-integrable function, f ∈ L2((Formula presented.)), to its d ≥ 1 input variables, x = {x1, …, xd}, with support (Formula presented.). The prototype of this approach, Sobol's method is a generalization of the analysis of variance (ANOVA) to d > 2 independent input variables and decomposes y, as sum of elementary functions of zeroth-, first-, second-, up to dth-order. This independence assumption is mathematically convenient but may not be borne out of the causal or correlational relationships between the x's. This paper is concerned with variance-based sensitivity analysis (SA) for correlated input variables, for example, multivariate dependencies in a posterior parameter distribution. We use high-dimensional model representation (HDMR) of Li et al. (2010, https://doi.org/10.1021/jp9096919), Li and Rabitz (2012, https://doi.org/10.1007/s10910-011-9898-0) and replace Sobol's elementary functions with so-called component functions with unknown expansion coefficients to disentangle the structural, correlative and total contribution of input factors. We contrast the default HDMR methodology with cubic B-splines and sequential coefficient estimation against its successor, HDMRext of Li and Rabitz (2012, https://doi.org/10.1007/s10910-011-9898-0), which uses polynomial component functions with an extended orthonormalized basis. Benchmark experiments confirm that HDMR and HDMRext parse out the structural and correlative contributions of input factors to the model output and infer an optimal experimental design with parameter correlation. Our last study applies HDMRext to probabilistic SA of a watershed model. The multivariate posterior parameter distribution supports model emulation and yields sensitivity indices that pertain to measured discharge data.