#Latin Hypercube Sampling Simulation Software Products Offer

Latin Hypercube Sampling Simulation Software Products Offer

After searching fow a while, I finally found a paper describing the LHC sampling (Swiler and Wyss 2004). The technique dates back to 1980 (even though the RISK manual describes LHS as a new sampling technique) when. It is a method for ensuring that each probability distribution in your model is evenly sampled which at first glance seems very appealing. Most risk analysis simulation software products offer Latin Hypercube Sampling (LHS).

, abbreviated as LW the fourth method is. An.More specifically, four alternative approaches for conditional stratified sampling are considered: three based on Latin hypercube samplingthe method of Stein , abbreviated in what follows as LS, the method of Iman and Conover , abbreviated as LI, and the conditional simulation method of Xu et al. Monte Carlo simulation (MCS) and Latin hypercube sampling (LHS). The paper of Swiler & Wyss presents a detailed example of the algorithm so anybody can check the results and the algorithm itself (pages 2-9 in the paper).Many methods exist for generating input parameters for model realizations including.

A novel extension of Latin hypercube sampling (LHSMDU) for multivariate models is developed here that increases the multidimensional uniformity of(* =HERE IS THE MATHEMATICA CODE WITH COMMENTS = *)A. Recent algorithms such as maximinLHS seek to maximize the minimum distance between model inputs in the multivariate space. The LHS is compared with the sequential Gaussian simulation available in GSLIB.In essence, the sample size ss serves to divide the sampling space into ss categories and then the U values are re-scaled to the new limits:including Monte Carlo simulation (MCS) an d Latin hypercube sampling (LHS).

Technometrics 21(2): 239-245.Stein M. A comparison of three methods for selecting values of input variables in the analysis of output from a computer code. LHC applies only to distributions where the inverse method exist, but for distributions like the normal and lognormal, the inverse method is an approximate one, so it would be better relying on another number generation method instead.In my opinion, it doesn't harm to implement the algorithm for medium size projects.KEY WORDS: MATHEMATICA, LATIN HYPERCUBE SAMPLING, MONTE CARLO SIMULATIONMcKay MD, Beckman RJ Conover WJ. Vose) that LHC is a relict of the past, carrying an overload of memory and computing time. An example of Latin hypercube sampling (*scaleu is the LHS re-scaling, very simple indeed! *)Scaleu := u (1/ss) + ((i - 1)/ss) (* set function as listable, capable of handling lists*)Un = RandomVariate, ImageSize -> 350](* =END OF CODE HERE = *)So, it seems clear that LHC improves the shape of the target distribution, even at the largest sample size (n=10,000)Some people argue (for example D.