Introduction Latin hypercube sampling (LHS) is a form of that can be applied to multiple variables. The method commonly used to reduce the number or runs necessary for a Monte Carlo simulation to achieve a reasonably accurate random distribution. LHS can be incorporated into an existing Monte Carlo model fairly easily, and work with variables following any analytical probability distribution. Monte-Carlo simulations provide statistical answers to problems by performing many calculations with randomized variables, and analyzing the trends in the output data. There are many resources available describing (,, ). The concept behind LHS is not overly complex.

Jul 23, 2014 - Latin Hypercube sampling (LHS) aims to spread the sample points more. If you are implementing your own simulation software, and don't.

Variables are sampled using a even sampling method, and then randomly combined sets of those variables are used for one calculation of the target function. The sampling algorithm ensures that the distribution function is sampled evenly, but still with the same probability trend. Figure 1 and figure 2 demonstrate the difference between a pure random sampling and a stratified sampling of a log-normal distribution. (These figures were generated using different versions of the same software. Differences within the plot, such as the left axis label and the black lines, are due to ongoing development of the software application and are not related to the issue being demonstrated.) Figure 1.

Randy vanwarmer biography. A cumulative frequency plot of “recovery factor”, which was log-normally distributed with a mean of 60% and a standard deviation of 5%. 500 samples were taken using the stratified sampling method described here, which generated a very smooth curve. A cumulative frequency plot of “recovery factor”, which was log-normally distributed with a mean of 60% and a standard deviation of 5%. 500 random samples were taken. Process Sampling To perform the stratified sampling, the cumulative probability (100%) is divided into segments, one for each iteration of the Monte Carlo simulation.

A probability is randomly picked within each segment using a uniform distribution, and then mapped to the correct representative value in of the variable’s actual distribution. A simulation with 500 iterations would split the probability into 500 segments, each representing 0.2% of the total distribution. For the first segment, a number would be chosen between 0.0% and 0.2%. For the second segment, a number would be chosen between 0.2% and 0.4%. This number would be used to calculate the actual variable value based upon its distribution.