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Simple Random

Simple Random

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Simple Random

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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. Because the sequence of samples is random, clustering may occur in the design point distribution.

figure_9

Figure 1: Illustration of Simple Random Sampling

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, you can change the following settings of Simple Random from the Settings tab.

Parameter

Default

Range

Description

Number of Runs

(npt)

100

> 0

Number of designs to be evaluated.

Random Seed

(iseed)

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.

Apply User Correlations

true

true or false

Apply user specified correlations on the data.