"Torsten" wrote in message <email@example.com>... > "Francesco Perrone" <firstname.lastname@example.org> wrote in message <email@example.com>... > > Hi all, > > > > I have got a question concerning normal distribution (with mu = 0 and sigma = 1). > > > > Let say that I firstly call randn or normrnd this way > > > > x = normrnd(0,1,[4096,1]); % x = randn(4096,1) > > > > Now, to assess how good x values fit the normal distribution, I call > > > > [a,b] = normfit(x); > > > > and to have a graphical support > > > > histfit(x) > > > > Now come to the core of the question: if I am not satisfied enough on how x fits the given normal distribution, how can I optimize x in order to better fit the expected normal distribution with 0 mean and 1 standard deviation?? Sometimes because of the few representation values (i.e. 4096 in this case), x fits really poorly the expected Gaussian, so that I wanna manipulate x (linearly or not, it does not really matter at this stage) in order to get a better fitness. > > > > I'd like remarking that I have access to the statistical toolbox. > > > > I thank you all in advance. > > Increase the number of sampling points (4096 in your example) > or > try another random number generator for a normally distributed random variable. > > Best wishes > Torsten.
It's quite a simplistic method.
Unfortunately, I cannot magnify the number of representations because of some reasons I will not explain here in detail (theory beyond the code I am writing). Besides, what else random generator may I use?
I do believe that is a way to "force" data better fitting the expected normal distribution.