Latin HyperCube
An approach which can yield precise estimates of output statistics with a lesser number of samples than simple random sampling.
The Latin HyperCube method uses a constrained or stratified sampling scheme.
- The range of each random variable is divided into non-overlapping intervals on the basis of equal probability.
- One value from each interval is selected at random with respect to the probability density in the interval.
- The values thus obtained for are paired in a random manner with the values of . These pairs are combined in a random manner with the values of to form triplets and so on, until k-tuplets are formed.
Usability Characteristics
- A stratified sampling scheme like Latin HyperCube offers the advantage of selecting random variable values that are uniformly spread across the range of random variables while taking into account the probability density function of those random variables.
- A correlation structure can be specified to reflect the correlation existing between random variables. Applying a correlation structure can be costly for a large number of input variables.
Settings
Parameter | Default | Range | Description |
---|---|---|---|
Number of Runs | 100 | > 0 | Number of new designs to be evaluated. |
Random Seed | 1 | Integer 0 to 10000 |
Controlling repeatability of
runs depending on the way the sequence of random numbers is
generated.
|
Apply User Correlations | On | Off or On | Apply user specified correlations on the data. |