# Sum of uniform random variables on simplex

Let $X,X'$ be two independent uniform random variables on $n$-dimensional simplex $\Delta_n= \{(x_1,\ldots,x_n):x_i \geq 0, \sum x_i \leq 1\}$. I am trying to find the probability distribution of their sum $$Y= X+X'$$ More specifically I am interested in finding the differential entropy of their sum, $h(Y)$. $$h(Y)= -\int f_Y(y) \log(f_Y(y)) \ dy$$

The convolution integrals are tending to be too messy. I couldn't find any other trick apart from convolution.

• For $n=2$ I get that the density is proportional to $y_1y_2$ for $y_1+y_2\lt1$ or $y_1\gt1$ or $y_2\gt1$ and to $y_1y_2-(y_1+y_2+1)^2$ otherwise. That doesn't bode very well for finding a closed form for the entropy -- Wolfram|Alpha can't solve the integral for the second case. – joriki Sep 10 '15 at 5:50

A very crude asymptotic:

Consider first the entropy of $X$. Because it's uniform inside the $n-$ simplex, with volume $1/n!$, its entropy is $$H(X)=-\log(n!) \tag{1}$$

The density of the sum $S=\sum{X_i}$ is a beta density $f(S)=n S^{n-1}$, which means that $X$ tends to concentrate around $\sum X_i=1$ for large $n$. Further, over this region, the (multivariate) $X$ is asympotically equivalent to a set of $n$ iid exponential variables (lets call them $Z_i$) with mean $1/n$ (or $\lambda = n$).

Indeed, the density of these $Z$ variables is $$H(Z)=n H(Z_i)=n (1-\log(n))\tag{2}$$ which agrees (asympotically) with $(1)$. This gives some (non rigorous) support to the following approximation: given that $X\sim Z$, instead of $Y = X+X'$, let's consider $W=Z+Z'$. Then $W_i$ (sum of two exponentials with parameters $\lambda=n$) follows a gamma distribution $(2,1/\lambda)$, with mean $E(W_i)=2/n$ and entropy

$$H(W_i)=1-\log(n)+\gamma$$

Hence $$H(W)=n(1-\log(n)+\gamma)$$

We can then conjecture $$H(Y) \approx n(1-\log(n)+\gamma)$$ or perhaps $$H(Y) = -\log(n!) + n \gamma + o(n)$$

Update (from OP comment): By the same reasoning, we can approximate $H(X-X')$:

The difference of two exponentials with paramenter $\lambda$ is a zero mean laplacian with scale $\lambda^{-1}$; and its entropy is $\log(2/\lambda)+1$

Hence $$H(X-X')\approx n (1+ \log(2) - \log(n))\approx -\log(n!) + n \log(2)$$

And

$$\frac{H(X-X')+\log(n!)}{H(X+X')+\log(n!)} \approx \frac{n \log(2) + o(n)}{n \gamma + o(n)} \to \frac{\log(2)}{\gamma}\approx 1.2008461$$

Remember that this approximation is only expected (by me at least) to work for large $n$. Further, the $o(n)$ term has probably a leading $O(\log(n))$ term, so the convergence can be slow.

I'd like to know if this fits your empirical results (if you have them), or the context.

• Can we do a similar analysis for $H(X-X')$? My goal is to study the asymptotics of $\frac{H(X-X')+\log(n!)}{H(X+X')+\log(n!)}$ – pikachuchameleon Feb 15 '16 at 4:27
• @pikachuchameleon See my addition – leonbloy Feb 15 '16 at 12:45
• While the above asymptotics are fairly intuitive, I tried to numerically compute the above quantities and plotted them. Surprisingly, the above ratio is converging to $1$ instead of $1.2008$. I can send you the graph if you want. But I don't know how to include it in comments section. – pikachuchameleon Feb 15 '16 at 22:11
• @pikachuchameleon : I'd like to look at your numerical results, and how you got them, for sure - I suggest that you post them as your own answer. – leonbloy Feb 15 '16 at 22:44
• I added the answer. Please check it. Surprisingly, the ratio is approaching 1. – pikachuchameleon Feb 19 '16 at 23:26

Let $Z=X+Y$ and $Z'=X-Y$. Since $X$ and $Y$ are i.i.d on $\Delta_n$, the densities of $Z$ and $Z'$ are given by: \begin{align*} f_Z(z) &=\int_{\Delta_n \cap \left(-\Delta_n+z \right)} dx\\ f_Z'(z) &=\int_{\Delta_n \cap \left(\Delta_n+z \right)} dx\\ \end{align*} and the entropies of $Z$ and $Z'$ are similarly given by: \begin{align} h(Z) &= \int_{\Delta_n + \Delta_n} f_Z \log \frac{1}{f_Z}\\ h(Z') &= \int_{\Delta_n -\Delta_n} f_{Z'} \log \frac{1}{f_{Z'}}\\ \end{align} After using Monte Carlo integration to compute the required integrals I obtained the following results for the ratio $\frac{h(Z')+\log n! }{ h(Z)+\log n!}$: • I've done some simulations myself, by different method (nearest neighbour estimation), and my (non conclusive at all) results don't seem to agree with this (ratio keeps above 1.05 at least, for dimensions up to 24... Anyway, entropy estimation is hard, and more in high dimensions – leonbloy Feb 20 '16 at 1:40
• I agree. I am equally surprised about the result. I expected it would converge to 1.200 approximately. But I don't know. Are you sure with the ratio being above 1.05 always? Mine always converged to 1. – pikachuchameleon Feb 20 '16 at 5:22
• I'm not sure at all. But anyway your graph looks funny, it seem to have more noise for small dimensions, it should be the other way... Is the code too complicated to post? – leonbloy Feb 20 '16 at 13:47
• Code is a little bit lengthy, that's why I didn't post. No, it's not complicated at all. It's fairly simple Monte Carlo sampling and integration. – pikachuchameleon Feb 26 '16 at 16:10