# Does high pdf imply more samples in this region?

Suppose that I have a random vector $Z = [Z_1,\dots,Z_d]$ where $Z_d \sim \text{beta}(30,30)$ i.i.d. We know that the pdf of this random vector is maximized at $[0.5,\dots,0.5]$. Consider 2 cases, when $d=2$ and $d=20$. I generated samples of $Z$ using Matlab. Using these samples, I computed the euclidean norm of each from $[0.5,\dots,0.5]$ and plotted the histogram of the euclidean norm below, for $d=2$, left, and $d=20$, right.

It is expected that the ranges on the x-axis are different and that the euclidean norms for the 20-D case are larger. But why is the skewness different? The histogram for the 2-D case is more skewed to the right which means that more of its samples are closer to $[0.5,0.5]$. However, the same cannot be said for the 20-D case. It appears that there are fewer samples closer to $[0.5,\dots,0.5]$ than are farther from it which is strange as the pdf is maximum at $[0.5,0.5,\dots,0.5]$...

Can anyone explain how to make sense of this? • I think so. PDFs can be thought of as glorified histograms, so if a lot of the mass is centered somewhere, much of the data lies there. – Sean Roberson Aug 22 '17 at 21:27

Your 20D version could take values between $0$ and $\sqrt{5}\approx 2.2$, but has a mean of about $0.283$ and a standard deviation of about $0.044$ and is almost normally distributed with minimal skewness, and would get closer to being normally distributed at even higher dimensions. Virtually no values are very close to $0$, as your histograms show.
The lack of points close to $0$ in the 20D case should not be a surprise: it only takes one of the $20$ random variables to have a substantially non-zero value to ensure the distance is substantial and this is more likely in higher dimensions: in machine learning this is the curse of dimensionality
Your 2D version could take values between $0$ and $\sqrt{\frac12}\approx 0.7$, but has a mean of about $0.08$ and a standard deviation of about $0.041$. It cannot be close to normally distributed as the density must fall to zero at $0$, which is within two standard deviations of the mean; the upper bound is much looser so the right tail is heaver, leading to right skewness of the distribution
• I think $500\,000$ iterations are enough to establish an accurate impression. More iterations might yield a histogram that is smoother, but not one with a fundamentally different shape. // You might consider using kernel density estimators as better approximations to density functions. – BruceET Aug 23 '17 at 1:24