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Re: manipulate data to better fit a Gaussian Distribution
Posted:
Mar 19, 2013 6:50 AM
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"Torsten" wrote in message <ki9fdu$91c$1@newscl01ah.mathworks.com>...
> "Francesco Perrone" <francesco86perrone@yahoo.it> wrote in message <ki9dra$56c$1@newscl01ah.mathworks.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.
Regards,
Francesco
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