Latin Hypercube Sampling Algorithm
The majority of literature regarding optimized Latin hypercube sampling (OLHS) is devoted to increasing the efficiency of these sampling strategies through the. Instances of the use of random LHS are prevalent in tailored software packages, examples include 'DiceDesign' (see [36]) by Franco, Dupuy and Roustant, and. Latin Hypercube Sampling. Latin hypercube sampling (LHS), a stratified-random procedure, provides an efficient way of sampling variables from their distributions. Latin hypercube sampling (LHS) is a statistical method for generating a near-random sample of parameter values from a multidimensional distribution. The sampling method is often used to construct computer experiments or for Monte-Carlo integration. The LHS was described by McKay in 1979. An independently.
DescriptionX = lhsdesign(n,p) returnsan n-by- p matrix, X,containing a latin hypercube sample of n valueson each of p variables. For each column of X,the n values are randomly distributed with onefrom each interval (0,1/n), (1/n,2/n)., (1-1/n,1), and they are randomly permuted.X = lhsdesign(.,'smooth','off') producespoints at the midpoints of the above intervals: 0.5/n, 1.5/n., 1-0.5/n. The default is 'on'.X = lhsdesign(.,'criterion', criterion) iterativelygenerates latin hypercube samples to find the best one according to criterion,which can be 'none', 'maximin',or 'correlation'.