Latin hypercube sampling: Difference between revisions

Revision as of 13:59, 18 August 2006

Revision as of 21:16, 26 September 2006

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[[Image:LHSsampling.png|100px]]

In two dimensions the difference between random sampling, Latin Hypercube sampling and orthogonal sampling can be explained as follows:

I) In '''random sampling''' new sample points are generated without taking into account the previously generated sample points. One does thus not necessarily need to know beforehand how many sample points that are needed.

II) In '''Latin Hypercube sampling''' sampling one must first decide how many sample points to use and for each sample point remember in which row and column the sample point was taken.

III) In '''Orthogonal Sampling''' Sampling, the sample space is divided into equally probable subspaces, the figure above showing four subspaces. All sample points are then chosen simultaneously making sure that the total ensemble of sample points is a Latin Hypercube sample and that each subspace is sampled with the same density.

To put it in layman terms, orthogonal sampling ensures that the ensemble of random numbers is a very good representative of the real variability, LHS sampling ensures that the ensemble of random numbers is a good representative of the real variability whereas traditional random sampling (sometimes called brute force) is just an ensemble of random numbers without any guarantees.