Convolution of two independent normally distributed random variables
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If x and y are independent and both normal with mean=5, and v=4, then z=x+y should be normal with mean=10, and v=8. Why can't I proof this using convolution in Matlab.
a=linspace(-5,15,10000); x=normpdf(a,5,2); y=normpdf(a,5,2); z=conv(x,y); m=mean(z); sd=std(z);
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Réponses (3)
Wayne King
le 14 Sep 2012
Modifié(e) : Wayne King
le 14 Sep 2012
Hi, you are confusing things here a bit. You don't want to consider the mean and standard deviations of the PDFs. You want to consider the mean and standard deviation of the random variables. Those are very different things.
x = normrnd(5,2,1000,1);
y = normrnd(5,2,1000,1);
z = x+y;
mean(z), var(z)
To see that the N(0,8) (here I mean variance 8) is good fit to z
x = normrnd(5,2,1000,1);
y = normrnd(5,2,1000,1);
z = x+y;
mu = 10;
sigma = sqrt(8);
zmin = min(z); zmax = max(z); zrange = range(z);
binw = zrange / 30;
edges = zmin + binw*(0:30);
n = histc(z, edges);
bar(edges, n, 'histc');
hold on
xx = zmin:(zrange/1000):zmax;
plot(xx, binw*length(z)*normpdf(xx,mu,sigma), 'r');
title('Normal Fit');
The PDF of z is the convolution of the PDFs of x and y
2 commentaires
Rick Rosson
le 14 Sep 2012
Please try:
mean(x)
std(x)
What does it show? Is it what you expected? Why or why not?
Likewise:
mean(y)
std(y)
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Star Strider
le 14 Sep 2012
Modifié(e) : Star Strider
le 14 Sep 2012
Since the convolution is based on the variables described by a specific distribution, it's likely best to look at the inverse normal distribution:
p = linspace(0.001,0.999,10000);
x = norminv(p,5,2);
y = norminv(p,5,2);
These give the values of a mean of 5 and a standard deviation of 2:
mx = mean(x);
sx = std(x);
my = mean(y);
sy = std(y);
and these give the values of a mean of 10 and standard deviation of 4:
z = x + y;
mz = mean(z);
sz = std(z);
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