Minimize error between data distribution and expected distribution
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I have a 3 set of data which are expected to:
1) 1st data-block to approach a Gaussian distribution with mu = 0 and sigma = 1;
2) 2nd data-block to approach a Gaussian distribution with mu = 0 and sigma = .8;
3) 3rd data-block to approach a Gaussian distribution with mu = 0 and sigma = .5;
Each data-block has only a limited number of representations (generally between 2048 and 8192) and because of some filter effects drawn by the specific code I use, they will not exactly match the corresponding expected distribution.
The point is that, although what it implies in terms of manipulation, I want each data-block to minimize the discrepancy between actual and expected distribution. It's to be remarked that I won't increase the number of representations, due to some need I will not explain in detail.
Generally, the first data-block, respect to the normal Gaussian distribution, looks like the followinf figure:
I was thinking to use lsqcurvefit for this purpose.
What would you suggest?
Wouter on 20 Mar 2013
Do you know this function:
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