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