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.įigure 2.
#Latin hypercube sampling and staratified 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.)įigure 1. (These figures were generated using different versions of the same software.
Figure 1 and figure 2 demonstrate the difference between a pure random sampling and a stratified sampling of a log-normal distribution. The sampling algorithm ensures that the distribution function is sampled evenly, but still with the same probability trend. 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 concept behind LHS is not overly complex. There are many resources available describing Monte-Carlo ( history, examples, software). Monte-Carlo simulations provide statistical answers to problems by performing many calculations with randomized variables, and analyzing the trends in the output data. LHS can be incorporated into an existing Monte Carlo model fairly easily, and work with variables following any analytical probability distribution.
The method commonly used to reduce the number or runs necessary for a Monte Carlo simulation to achieve a reasonably accurate random distribution. Latin hypercube sampling (LHS) is a form of stratified sampling that can be applied to multiple variables.
0 kommentar(er)
