Gaussian samples – Part (3)

Generate 1000 Gaussian samples in 2-D using central limit theorem

In short, the central limit theorem allows us to easily generate Gaussian samples in 2-D, whose x and y coordinates are the Gaussian sums of many uniform samples. However, we still need to rescale these x and y coordinates so that they return to standard normal (mean of 0 and standard deviation of 1).

Rescale Gaussian samples

Rescaling the Gaussian samples means we have to subtract each sum by its mean and divide by its standard deviation.



As a result, the Gaussian samples that represent the x and y coordinates can be normalized as follows:

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