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The [[statistical]] method '''Latin hypercube sampling''' (LHS) was developed and invented by [[Ronald L. Iman]], J. C. Helton, and J. E. Campbell, et al to generate a distribution of plausible collections of parameter values from a [[multidimensional distribution]].
Their paper ''An approach to sensitivity analysis of computer models, Part I. Introduction, input variable selection and preliminary variable assessment.'' appeared in the Journal of Quality Technology in [[1981]]. Earlier, M.D. McCay already described this techneque in McKay, M. D., W. J. Conover and R. J. Beckman. 1979.
''A Comparison of Three Methods for Selecting Values of Input Variables in the Analysis of Output from a Computer Code'', in ''Technometrics 21: 239-245''.
In the context of statistical sampling, a square grid containing sample positions is a Latin square if (and only if) there is only one sample in each row and each column. A Latin hypercube is the generalisation of this concept to an arbitrary number of dimensions, whereby each sample is the only one in each axis-aligned hyperplane containing it.
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