Publications

A method for extending the size of Latin Hypercube Sample

Sallaberry, Cedric J.; Helton, Jon C.

Latin Hypercube Sampling (LHS) is widely used as sampling based method for probabilistic calculations. This method has some clear advantages over classical random sampling (RS) that derive from its efficient stratification properties. However, one of its limitations is that it is not possible to extend the size of an initial sample by simply adding new simulations, as this will lead to a loss of the efficient stratification associated with LHS. We describe a new method to extend the size of an LHS to n (>=2) times its original size while preserving both the LHS structure and any induced correlations between the input parameters. This method involves introducing a refined grid for the original sample and then filling in empty rows and columns with new data in a way that conserves both the LHS structure and any induced correlations. An estimate of the bounds of the resulting correlation between two variables is derived for n=2. This result shows that the final correlation is close to the average of the correlations from the original sample and the new sample used in the infilling of the empty rows and columns indicated above.