latin hypercube sampling better than monte carlo

It can also be used to force the sampling of tails of distributions, although it should be remembered that LH stratification applies to each individual input variable, and it would not force the simultaneous sampling of tail values for more than one input. In general, LH sampling would be favourable when testing and developing a model (running only a small number of iterations), or when running a model that is so large that only a small number of iterations can be conducted. for 1000 iterations, exactly one value above the P99.9 would be drawn, whereas for MC sampling either none, one or several samples may be drawn). Latin hypercube sampling operates in the following manner to generate a sample of size nS from xx 1,x 2,x nX in consistency with the distributions D 1,D 2,D nX indicated in Eq. a small number of iterations is used), and would also ensure that tail samples of the input distributions are drawn (e.g. Thus, Latin hypercube sampling displays properties between random sampling, which involves no stratification, and stratified sampling, which stratifies on S su. In so doing, it achieves a more uniform sampling over each input distribution than standard Monte Carlo, where the natural randomness usually results in more clumped sampling. When generating its n runs, it samples exactly once from each interval. It would avoid potential non-representative clustering of sampled values (particularly when small sample sizes are drawn i.e. Latin hypercube sampling (LHS) divides up each uncertain input into n equiprobable intervals. In theory, LH sampling would create a more representative sampling of the distribution. the cumulative curve is divided into equal intervals on the cumulative probability scale (0 to 1.0).

LH sampling involves a stratification of the input distribution i.e.

When using ( Monte Carlo software for risk analysis and risk assessment), a user may choose between the Monte Carlo (MC) and Latin Hypercube (LH) sampling types.

Latin hypercube sampling better than monte carlo