The Latinized partially stratified sampling method is then applied to identify the best sample strategy for uncertainty quantification on a plate buckling problem. Optimal Sliced Latin Hypercube Designs Optimal Sliced Latin Hypercube Designs Shan BA, William R. Latin hypercube sampling (LHS) is a stratified random procedure that provides an efficient way of sampling variables from their multivariate distributions. Several high-dimensional numerical examples highlight the strengths and limitations of the method. Utilizing an LSS on the subspaces of a PSS provides a sampling strategy that reduces variance associated with both main effects and variable interactions and can be designed specially to minimize variance for a given problem.
Its key property is strati-fying each univariate margin, due to which it has also found wide applications in computer experiments (Welch et al. The LSS method is equivalent to an Orthogonal Array based LHS under certain conditions but is easier to obtain. Latin hypercube sampling was introduced by McKay, Conover and Beckman (1979) for numerically evaluating a multiple integral. To overcome these challenges, the PSS method is coupled with a new method called Latinized stratified sampling (LSS) that produces sample sets that are simultaneously SS and LHS.
#MATLAB LATIN HYPERCUBE SAMPLING CODE DOWNLOAD#
Challenges associated with the use of PSS designs and their limitations are discussed. Please check out for more tutorials.You can download 'lhsgeneral' from the following link.
Large sample properties of simulations using Latin hypercube sampling. PSS designs are shown to reduce variance associated with variable interactions, whereas LHS reduces variance associated with main effects. This MATLAB function returns an n-by-p matrix, X, containing a Latin hypercube sample of size n from a p-dimensional multivariate normal distribution with mean vector. lhsdesignmodified provides a latin hypercube sample of n values of each of p variables but unlike lhsdesign, the variables can range between any minimum. The variance of PSS estimates is derived along with some asymptotic properties. True SS and LHS are shown to represent the extremes of the PSS spectrum. Latin hypercube sampling (LHS) is generalized in terms of a spectrum of stratified sampling (SS) designs referred to as partially stratified sample (PSS) designs.
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