Latin hypercube sampling-based Monte Carlo simulation: Extension of the sample size and correlation control

Abstract

Latin hypercube sampling (LHS) is frequently used in Monte Carlo-type simulations for the probabilistic analysis of systems due to its variance reducing properties compared with random sampling. LHS allows for an extension of the sample size by doubling them or adding an even multiple of the sample size depending on the selection of the sample values. This can become a drawback of LHS compared to random sampling especially in the presence of a large sample size. In this chapter, a new approach to the multiple extension of a Latin hypercube samples is presented. The objective is to extend the sample size but to keep the increase in the number of realizations constant. It is of particular importance that the present approach is able to maintain the correlations between the input variables in the probabilistic analysis.