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normal distribution from data

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harley
harley 2013-9-10
is there a more efficient way to derive a normal distribution.
% Deriving Normal Distribution From the Data
x=0:1:12;
m=mean(Data);
s=std(Data);
p=(1/(s*sqrt(2*pi)))*exp((-(x-m).^2)/(2*s^2));
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Roger Stafford
Roger Stafford 2013-9-10
Image Analyst, it isn't 'x' that Harley is stating has the normal distribution. It is 'data' which isn't being specified here. The 'x' is the independent variable in the hypothesized normal distribution. A plot of
plot(x,p)
would give the theoretical normal distribution pdf values as functions of x for the mean and std which have been computed from 'data'.
Image Analyst
Image Analyst 2013-9-10
You're right - I messed up and thought that x was also the Data.

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Youssef Khmou
Youssef Khmou 2013-9-10
Here is another suggestion:
y=pdf('Normal',x,m,s);
plot(x,y);
  2 个评论
Image Analyst
Image Analyst 2013-9-10
It's fewer characters, so it's simpler to look at, but I doubt it's faster or more efficient (since there is more than one line of code inside that function), but I doubt he really wanted/needed more efficiency or speed anyway.
Youssef Khmou
Youssef Khmou 2013-9-11
Yes Mr @Image Analyst, the advantage i see is that this function gives a choice for other laws besides the Gaussian,

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更多回答（2 个）

Shashank Prasanna
Shashank Prasanna 2013-9-10
编辑：Shashank Prasanna 2013-9-10
Since this is normal distribution, the mean and std of the data are the maximum likelihood estimates for the normal distribution from the data.
Once you have the PDF, like you have in the last line of code as 'p', you could plot the PDF using x to span -4*sigma to +4*sigma:
x = -4*s:0.01:4*s
p=(1/(s*sqrt(2*pi)))*exp((-(x-m).^2)/(2*s^2));
plot(x,p)
You could use a wider range if you wanted to.
Roger Stafford
Roger Stafford 2013-9-10
You might try the Statistics Toolbox function 'normplot' to see how closely your 'data' comes to a normal distribution.

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