# Annulus distribution in n dimensions: normalising constant, normed mean and variance

I would like to know the normalising constant of a distribution which has the pdf,

$$f(x) \propto \sqrt{\frac{1}{2\pi\sigma^2}}\text{exp}(-\frac{(|x|-r_0)^2}{\sigma^2}),$$

where $$x\in\mathbb{R}^d$$ and $$|x|$$ is the Euclidean norm of $$x$$. This distribution looks like an annulus in two-dimensions, In higher dimensions, samples from it are normally-distributed centred on the surface of a hypersphere of radius $$r_0$$ (at least, I hope it is - please tell me if I am wrong).

Intuition (and a previous question), suggests to me that this distribution has an integral over space that equals the surface area of a hypersphere $$\mathbb{S}^{n-1}$$, multiplied by $$r_0^{n-1}$$, where $$n$$ is the number of dimensions of $$x$$. Basically, this accounts for taking a normal density and smearing it over higher dimensional space.

The trouble is that I am having trouble confirming my results in Mathematica. The following seems to work ok,

volS[n_] := (2 (Pi^(n / 2)) ) / Gamma[n / 2]

fPDF[z_, r0_, sigma_] :=
Block[{n = Length@z}, (1 / (volS[n] r0^(n - 1)))
PDF[NormalDistribution[r0, sigma], Norm[z]]]

NIntegrate[fPDF[{x, y}, 10, 1], {x, -Infinity, Infinity}, {y, -Infinity, Infinity}]


1.

But when I vary $$r_0$$ and $$\sigma$$ I get different answers. For example,

NIntegrate[fPDF[{x, y}, 20, 2], {x, -Infinity, Infinity}, {y, -Infinity, Infinity}]


0.772186

which seems to suggest I am wrong. However, I am not sure because I am a little fearful that the numerical integration is going awry.

Any ideas?

Also, I would have thought that the mean normed distance for this distribution should just be $$r_0$$ (as long as $$r_0>>\sigma$$). When I evaluate this, however, I get a different answer,

NIntegrate[Norm[{x,y}] fPDF[{x, y}, 10, 0.5], {x, -Infinity, Infinity}, {y, -Infinity, Infinity}]


7.17138

Any idea as to how to calculate the mean normed distance and variance?

Whilst all these examples are in two-dimensions, note that I am after n-dimensional results.

The numerical integration is not the issue. You also have to calculate a radial integral in order to determine the normalization constant. So, let's do it! Again, we apply polar coordinates:

ClearAll[r, r0, σ, n];
f = {r, r0, σ} \[Function] Evaluate[PDF[NormalDistribution[r0, σ], r]];
assumptions = And @@ {n ∈ Integers, n >= 1, r > 0, r0 > 0, σ > 0};
volS[n_] = (2 (Pi^(n/2)))/Gamma[n/2];
normalization[n_, r0_, σ_] = volS[n] Integrate[f[r, r0, σ] r^(n - 1), {r, 0, ∞},
Assumptions -> assumptions];


This defines a routine that creates a PDF for given dimension n, radius r0 and parameter σ:

makePDF[n_, r0_, σ_] := Function[
z,
Evaluate[Simplify[
f[Sqrt[Sum[Indexed[z, i]^2, {i, 1, n}]], r0, σ]/
normalization[n, r0, σ]
]]
];


For example, in dimension 2:

ρ = makePDF[2, r0, σ]


$$z \mapsto \frac{e^{\frac{\text{r0}^2-\left(\text{r0}-\sqrt{z_1^2+z_2^2}\right){}^2}{2 \sigma ^2}}}{\sqrt{2} \pi \sigma \left(\sqrt{\pi } \sigma e^{\frac{\text{r0}^2}{2 \sigma ^2}} \sqrt{\frac{\text{r0}^2}{\sigma ^2}} \text{erf}\left(\frac{\sqrt{\frac{\text{r0}^2}{\sigma ^2}}}{\sqrt{2}}\right)+\sqrt{\pi } \text{r0} e^{\frac{\text{r0}^2}{2 \sigma ^2}}+\sqrt{2} \sigma \right)}$$

Integrate[ρ[{x, y}], {x, -∞, ∞}, {y, -∞, ∞}, Assumptions -> assumptions]


1

• @ben18785 Good point. Thanks for the edit! – Henrik Schumacher Sep 26 '18 at 21:40
• No problem. Thanks for your work here. My differential geometry isn't what it once (or ever) was! – ben18785 Sep 26 '18 at 21:49
• You're welcome! – Henrik Schumacher Sep 26 '18 at 21:50