![latin hypercube sampling better than monte carlo latin hypercube sampling better than monte carlo](https://www.dl.begellhouse.com/pt/journals/ijuq/vol1/i3/p241-255/IJUQ0103-26754x.png)
LHS was described by Michael McKay of Los Alamos National Laboratory in 1979. There is some evidence to suggest that quasi-Monte Carlo methods perform better than Latin Hypercube when sampling over a small number of input variables17. The sampling method is often used to construct computer experiments or for Monte Carlo integration. than the classic Monte Carlo, but this advantage reduces. in larger dimensions it begins to degrade back to the performance of ordinary Monte Carlo. The development of the LHS technique will first be described and then compared with the Monte Carlo sampling technique. Latin hypercube sampling (LHS) is a statistical method for generating a near-random sample of parameter values from a multidimensional distribution. and Latin Hypercube Sampling, based on stratified nu- merical sets. Often, the LHS variance is much less than the IID variance. This has been successfully applied to rock slope stability analyses concerning plane and wedge failure and has been found to represent the original data more closely than could Monte Carlo sample sets of the same size. LHS, in essence, is a constrained randomisation sampling scheme which is sensitive to the extreme values of the data range. However computation time can be substantial if significant sampling errors are to be avoided.Īn alternative approach has been developed which uses the Latin Hypercube Sampling (LHS) approach to the same algorithms. Such analyses have been succesfully solved using a Monte Carlo Sampling approach based on well-established deterministic algorithms. A probabilistic approach to slope stability analysis allows the observed natural variability of many parameters to be taken into consideration.