# Latin Hypercube Simulation¶

**Step 1**The range of each input variable is stratified into isoprobabilistic cells,**Step 2**A cell is uniformly chosen among all the available cells,**Step 3**The random number is obtained by inverting the Cumulative Density Function locally in the chosen cell,**Step 4**All the cells having a common strate with the previous cell are put apart from the list of available cells.

where the sample of is obtained as described previously.

is the variance of the estimator of the probability of exceeding a threshold computed by the LHS technique,

is the variance of the estimator of the probability of exceeding a threshold computed by a crude Monte Carlo method.

the asymptotic confidence interval of order associated to the estimator is

where is the quantile from the reduced standard gaussian law .

It gives an unbiased estimate for (reminding that all input variables must be independent).

This method is derived from a more general method called ’Stratified Sampling’.

API:

See

`LHS`

Examples:

References:

Mc Kay, Conover, Beckman, A comparison of three methods for selecting values of input variables in the analysis of output from a computer code, Technometrics, 21 (2), 1979

Latin Hypercube Sampling and the Propagation of Uncertainty in Analyses of Complex Systems, J. Helton, F.J. Davis, 2002, SAND 2001-0417

The Design and Analysis of Computer Experiments by Thomas J. Santner, Brian J. Williams, and William I. Notz, Springer Verlag, New York 2003

A Central Limit Theorem for Latin Hypercube Sampling, Art B. Owen, 1992, Journal of the Royal Statistical Society. Series B (Methodological), Vol. 54, No. 2, pp. 541-551

Large Sample Properties of Simulations Using Latin Hypercube Sampling, Michael Stein, Technometrics, Vol. 29, No. 2 (May, 1987), pp. 143-151