Truncated Gaussian
Generate a pseudo-random vector X drawn from the truncated Gaussian distribution
Author: Bruno Luong

Ciao Bruno,

thanks for the code you've written. I have a similar doubt as Ricardo. I'd assume the code would divide the domain of the "range" in a number of points equal to the number of points specified in the command "n", TruncatedGaussian(sigma, [range], [n]);

However, the values in X are defined randomly, hence the mean estimated will always be different, even though the range and sigma are the same
>> range=[-3 3];
>> sigma=1;
>> [X meaneff sigmaeff] = TruncatedGaussian(sigma, range, [1 1e3]);
>> fprintf('mean(X)=%g, estimated=%g\n',meaneff, mean(X))
mean(X)=0, estimated=0.0555218
>> fprintf('sigma=%g, effective=%g, estimated=%g\n', sigma, sigmaeff, stdX)
sigma=1, effective=1, estimated=0.992506
>> [X meaneff sigmaeff] = TruncatedGaussian(sigma, range, [1 1e3]);
>> fprintf('mean(X)=%g, estimated=%g\n',meaneff, mean(X))
mean(X)=0, estimated=0.00445842
>> fprintf('sigma=%g, effective=%g, estimated=%g\n', sigma, sigmaeff, stdX)
sigma=1, effective=1, estimated=0.992506

This can be seen as well from hist, using same number of divisions.
Van I ask you if I correctly got your coding, and how to obtain an uniform division of the domain.