Probability Density Function using ksdensity is not normalized

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Ali
Ali 2014-6-28
回答： VladTheInstaller 2017-1-15
I have a vector "columnA" of N data points. I want to find the PDF. I use:
xi = min(columnA):1e-9:max(columnA);
f = ksdensity(columnA,xi);
plot(xi,f)
But when I use trapz to integrate f:
trapz(f)/length(xi)
the value is too far from 1. Even when increasing the range of xi, I still do not get reasonable value.

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回答（3 个）

VladTheInstaller
VladTheInstaller 2017-1-15
Actually, the output from ksdensity is normalized, but you will have to use numerical integration along the appropriate space. In your case,
trapz(xi,f)
should be close to 1.
Image Analyst
Image Analyst 2014-8-21
Why not use hist() or histc() to get the histogram? The histogram is essentially the probability density function.
Youssef Khmou
Youssef Khmou 2014-8-21
The ksdensity produces a Probability density function, no need to divide by the length of the x vector :
x=randn(200,1);
y=[min(x):0.1:max(x)];
p=ksdensity(x,y);
sum(p)
% plot(y,p)

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