% maintaining the ranks (and therefore rank correlations) from the % Transform each column back to the desired marginal distribution, % Get gridded or smoothed-out values on the unit interval % correlation structure - in this case multivariate normal % Generate a random sample with a specified distribution and % Copyright 1993-2010 The MathWorks, Inc. % sample before the marginals are adjusted to obtain X. % =LHSNORM(.) also returns Z, the original multivariate normal % 0.5/N we use a value having a uniform distribution on the % If 'ONOFF' is 'on' (the default), each column has points uniformly % normal cumulative distribution for that column''s marginal distribution. In other words, each column is a permutation If 'ONOFF' is 'off', each column has points equally spaced % X=LHSNORM(MU,SIGMA,N,'ONOFF') controls the amount of smoothing in the % is close to its theoretical normal distribution. % of each column is adjusted so that its sample marginal distribution % the multivariate normal distribution, but the marginal distribution % N from the multivariate normal distribution with mean vector MU % X=LHSNORM(MU,SIGMA,N) generates a latin hypercube sample X of size %LHSNORM Generate a latin hypercube sample with a normal distribution In matlab : edit lhsnorm : function = lhsnorm(mu,sigma,n,dosmooth) An edit of the lhsnorm function can probably answer your question.

Latin hypercube matlab