Latin hypercube sampling: Difference between revisions

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{{Short description|Statistical sampling technique}}

'''Latin hypercube sampling''' ('''LHS''') is a [[statistics|statistical]] method for generating a near-random sample of parameter values from a [[multidimensional distribution]]. The [[Sampling (statistics)|sampling method]] is often used to construct [[computer experiment]]s or for [[Monte Carlo integration]].<ref name = "C3M"/>

LHS was described by Michael McKay of Los Alamos National Laboratory in 1979.<ref name = "C3M">{{cite journal

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</ref> An equivalent technique was independently proposed by [[:lv:Vilnis Eglājs|Vilnis Eglājs]] in 1977.<ref>{{cite journal|last=Eglajs|first=V.|author2=Audze P. |title=New approach to the design of multifactor experiments|journal=Problems of Dynamics and Strengths|year=1977|series=35|pages=104–107|publisher=Zinatne Publishing House|___location=Riga|language=Russian}}</ref> It was further elaborated by [[Ronald L. Iman]] and coauthors in 1981.<ref>{{cite journal |last=Iman |first=R.L. |author2=Helton, J.C. |author3-link=James Edward Campbell |author3=Campbell, J.E. |title=An approach to sensitivity analysis of computer models, Part 1. Introduction, input variable selection and preliminary variable assessment |journal=Journal of Quality Technology |volume=13 |issue=3 |pages=174–183 |year=1981 |doi=10.1080/00224065.1981.11978748 }}</ref> Detailed computer codes and manuals were later published.<ref>{{cite book |last=Iman |first=R.L. |author2=Davenport, J.M. |author3=Zeigler, D.K. |title=Latin hypercube sampling (program user's guide) |year=1980 |osti=5571631}}</ref>

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.<ref name = "C3M"/>

When sampling a function of <math>N</math> variables, the range of each variable is divided into <math>M</math> equally probable intervals. <math>M</math> sample points are then placed to satisfy the Latin hypercube requirements; this forces the number of divisions, <math>M</math>, to be equal for each variable. This sampling scheme does not require more samples for more dimensions (variables); this independence is one of the main advantages of this sampling scheme. Another advantage is that random samples can be taken one at a time, remembering which samples were taken so far.