I have quite a straight-forward question. What I aim for is the generation of a certain set of random numbers with a normal distribution (
mu = 0,
sigma = 1).
Now, the best way to approach the above mentioned gaussian bell is to generate quite a large number of samples.
Unfortunately, in my case, I can only generate a retained subset of samples: usually around
2048 samples, which lead to a poor fit.
I am actually wondering if there is any way to improve the fitness, by first generating a larger set of samples or more subsets of the same size (as 10 times 2048 samples) and then pick those 2048 which approximate the best for desired mean and standard deviation (and the gaussian of course).
What would you suggest or how would you proceed?