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Normal Distribution Generation

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This generation rule produces random numbers using a normal distribution defined by a mean and a variance. As per the definition of the Wikipedia, the normal distribution is caraterized by :

In probability theory, the normal (or Gaussiandistribution is a very common continuous probability distribution. Normal distributions are important in statistics and are often used in the natural and social sciences to represent real-valued random variables whose distributions are not known.

All generated values will be produced arround the mean and with a dispersion controled by the variance / standard deviation. The higher the variance the higher the dispersion of generated value arround the mean.

If you look at the sample diagram from Wikipedia, you can see that with a variance of 0.2 the generated values are found closed to the mean (of zero here), while with the variance of 5 the values more spreaded.

Normal Distribution (Wikipedia)The implementation of the normal distribution generation relies on the BOOST Math Library and exposes four parameters, two for controling the generated values and two for controling the formating of those values.

Normal Distribution Generation

The precision parameter gives control over the number of digit to produce for each new value.

 

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