The conventional approach of sampling is commonly called Simple Random or Monte
Carlo. In Simple Random sampling, a pseudo-random number generator is used for generating
random numbers from 0 to 1.
Design points are generated by using the Inverse Transform method. Clustering may
occur in the design point distribution because the sequence of samples is
random. Figure 1.
Usability Characteristics
The statistical measures (such as mean or standard deviation) of a random
sample group requires large numbers of runs to converge the given
probability distribution’s statistical measures.
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
In the Specifications step, Settings tab, change method
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.
0
Random (non-repeatable).
>0
Triggers a new sequence of pseudo-random numbers, repeatable
if the same number is specified.
