Exploring multi-dimensional spaces: a Comparison of Latin Hypercube and Quasi Monte Carlo Sampling Techniques

By Sergei S. Kucherenko, Daniel Albrecht, Andrea Saltelli. 2015

131 citations recorded by the source corpus.

Three sampling methods are compared for efficiency on a number of test problems of various complexity for which analytic quadratures are available. The methods compared are Monte Carlo with pseudo-random numbers, Latin Hypercube Sampling, and Quasi Monte Carlo with sampling based on Sobol sequences. Generally results show superior performance of the Quasi Monte Carlo approach based on Sobol sequences in line with theoretical predictions. Latin Hypercube Sampling can be more efficient than both Monte Carlo method and Quasi Monte Carlo method but the latter inequality holds for a reduced set of function typology and at small number of sampled points. In conclusion Quasi Monte Carlo method would appear the safest bet when integrating functions of unknown typology.

Subfields: Numerical Analysis, Statistical and Nonlinear Physics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/166fffd505409849d5b123324bd915ed01704cad

Citations in the corpus

• The Future of Sensitivity Analysis: An essential discipline for systems modeling and policy support (2020, unknown): “The superior performance of Sobol ’ low-discrepancy sequences over random sampling has been demonstrated in several studies (Gilquin et al., 2020; Gilquin et al., 2017; Sheikholeslami et al., 2017; Rugama et al., 2018; Kucherenko et al., 2011; Kucherenko et al., 2015).”