Drexel dragonThe Math ForumDonate to the Math Forum



Search All of the Math Forum:

Views expressed in these public forums are not endorsed by Drexel University or The Math Forum.


Math Forum » Discussions » Software » comp.soft-sys.matlab

Topic: manipulate data to better fit a Gaussian Distribution
Replies: 6   Last Post: Mar 19, 2013 11:15 AM

Advanced Search

Back to Topic List Back to Topic List Jump to Tree View Jump to Tree View   Messages: [ Previous | Next ]
Francesco Perrone

Posts: 39
Registered: 5/2/12
Re: manipulate data to better fit a Gaussian Distribution
Posted: Mar 19, 2013 6:50 AM
  Click to see the message monospaced in plain text Plain Text   Click to reply to this topic Reply

"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



Point your RSS reader here for a feed of the latest messages in this topic.

[Privacy Policy] [Terms of Use]

© Drexel University 1994-2014. All Rights Reserved.
The Math Forum is a research and educational enterprise of the Drexel University School of Education.