## Tuesday, November 10, 2015

### Random subdivisions of an interval

This is a classical problem, but I find it very appealing.

Drop $n$  points $X_1,\dotsc, X_n$  at random in the unit interval, uniformly and independently. These  points     divide the interval into $n+1$  sub-intervals.  What is the expected length of the longest of these intervals? What is its asymptotic behavior for $n$ large?

I was surprised to find out that the  problem  of finding  the expectation of the largest segment   was solved   in late 19th century.    What follows   is a more detailed presentation  of the arguments in Sec. 6.3 of the book  Order Statistics   by H.A. David and  H.N. Nagaraja.

Relabel  by $Y_1,\dotsc, Y_n$, the points $\{X_1,\dotsc, X_n\}\subset [0,1]$ so that  $Y_1\leq \cdots \leq Y_n$.  We set

$$Y_0=0,\;\;Y_{n+1}=1,\;\;\;\;L_n= \max_{1\leq i\leq n+1} Y_i-Y_{i-1},.$$

We want to determine  the expectation $\newcommand{\bE}{\mathbb{E}}$ $E_n$ of $L_n$.

The probability distribution  of $\vec{X}=(X_1,\dotsc, X_n)$  $\newcommand{\bone}{\boldsymbol{1}}$   is

$$p_{\vec{X}}(dx)=\bone_{C_n}(x_1,\dotsc, x_n) dx_1\cdots dx_n, \;\;C_n=[0,1]^n,$$

where $\bone_A$ denotes the indicator function of a subset $A$ of a given set $S$.

The probability distribution of $\vec{Y}=(Y_1,\dotsc, Y_n)$ is

$$p_{\vec{Y}}(dy)= n! \bone_{S_n}(y_1,\dotsc, y_n) dy_1\cdots dy_n,$$

where $S_n$ is the simplex $\newcommand{\bR}{\mathbb{R}}$

$$T_n=\bigl\{ y\in\bR^n;\;\;0\leq y_1\leq \cdots \leq y_n\leq 1\,\bigr\}.$$

The   points $Y_1,\dotsc, Y_n$ determine $n+1$ segments on $[0,1]$ of lengths
$$S_i= Y_{i}-Y_{i-1},\;\; i=1,\dotsc, n+1.$$
The resulting map $\vec{Y}\to\vec{S}=\vec{S}(\vec{Y})$ induces a linear bijection

$$T_n\to \Delta_n=\bigl\{\, (s_1,\dotsc, s_{n+1})\in\bR_{\geq 0}^{n+1};\;\;s_1+\cdots+s_{n+1}=1\,\bigr\}.$$

This  shows that the probability density is $\DeclareMathOperator{\vol}{vol}$

$$p_{\vec{S}}(ds) =\frac{1}{\vol(\Delta_n)} dV_{\Delta_n},$$

where $dV_{\Delta_n}$ denote the usual  Euclidean volume density on  the standard simplex $\Delta_n$. This description  shows that  the random vector $\vec{S}$ is exchangeable, i.e., the probability  density  of $\vec{S}$ is invariant under permutation of the components. Thus the  joint probability distribution of any group  of $k$ components of $\vec{S}$,   $k\leq n$, is the same as the joint distribution of the first $k$-components $S_1,\dotsc, S_k$.

The joint  probability distribution of the first $n$ components is

$$p_n(ds)= n! \bone_{D_n}(s_1,\dotsc,s_n) ds,$$

where $D_n$ is the simplex

$$D_n=\{\, (s_1,\dotsc, s_n)\in\bR^n_{\geq 0};\;\;s_1+\cdots +s_n\leq 1\,\}.$$

Indeed, $(S_1,\dotsc, S_n)\in D_n$ and

$$ds=|ds_1\cdots ds_n|=|d y_1\wedge d(y_2-y_{1})\wedge \cdots \wedge d(y_{n}-y_{n-1})|=|dy_1\cdots d y_n|.$$

The  joint density of the first $n-1$ components $(S_1, \dotsc, S_{n-1})$ is

$$p_{n-1}(s_1,\dotsc, s_{n-1}) = n! \int_0^\infty \bone_{D_n}(s_1,\dotsc, s_n) ds_n$$

$$= n! \bone_{D_{n-1}}(s_1,\dotsc, s_{n-1})\int_0^{1-s+1-\cdots-s_{n-1}} ds_n= n! \bone_{D_{n-1}}(s_1,\dotsc, s_{n-1})(1-s_1-\cdots-s_{n-1})$$

The joint density of the first $(n-2)$ components is

$$p_{n-2}(s_1,\dots, s_{n-2})=\int_0^\infty p_{n-1}(s_1,\dotsc, s_{n-1}) ds_{n-1}$$

$$= n!\bone_{D_{n-2}}(s_1,\dotsc, s_{n-2})\int_0^{1-s_1-\cdots-s_{n-2}} (1-s_1-\cdots -s_{n-1}) d s_{n-1} = \frac{n!}{2!} \bone_{D_{n-2}}(s_1,\dotsc, s_{n-2})(1-s_1-\cdots -s_{n-2})^2.$$

Iterating, we deduce that the joint probability density of the the first $k$ variables is

$$p_k(s_1,\dotsc, s_k) =\frac{n!}{(n-k)!}p_{D_k}(s_1,\dotsc, s_k) (1-s_1-\cdots-s_k)^{n-k}.$$

If $1\leq k\leq n$,  then$\newcommand{\bP}{\mathbb{P}}$

$$\bP[S_1>s,\dotsc, S_k>s]=\frac{n!}{(n-k)!}\int_s^\infty\cdots\int_s^\infty p_{D_k}(s_1,\dotsc, s_k) (1-s_1-\cdots-s_k)^{n-k} ds_1\dotsc ds_k$$

$$=\frac{n!}{(n-k)!}\int_s^1ds_1\int_s^{1-s_1}ds_2\cdots\int_s^{1-s_1-\cdots-s_{k-1}} (1-s_1-\cdots-s_k)_+^{n-k} ds_k =(1-ks)_+^n,\;\;x_+:=\max(x,0).$$

Also

$$\bP[S_1>s,\dotsc, S_n>s, S_{n+1}>s] = \bP[S_1>s,\dotsc, S_n>s, S_1+S_2+\cdots +S_n\leq 1-s]$$

$$=n!\int_{\substack{s_1,\dotsc s_{n}>s\\ s_1+\cdots +s_{n}\leq 1-s}}ds_1\cdots d s_{n}$$

$$=n!\int_{\substack{s_1,\dotsc s_{n-1}>s\\ s_1+\cdots +s_{n-1}\leq 1-2s}}ds_1\cdots d s_{n-1}\int_s^{1-s-s_1-\cdots -s_{n-1}} d s_n$$

$$= n!\int_{\substack{s_1,\dotsc s_{n-1}>s\\ s_1+\cdots +s_{n-1}\leq 1-2s}} (1-2s-s_1-\cdots -s_{n-1}) ds_1\cdots ds_{n-1}$$

$$=n!\int_{\substack{s_1,\dotsc s_{n-2}>s\\ s_1+\cdots +s_{n-2}\leq 1-3s}}ds_1\cdots ds_{n-2}\int_s^{1-2s-s_1-\cdots -s_{n-2}} (1-2s-s_1-\cdots -s_{n-1}) d s_{n-1}$$

$$=\frac{n!}{2!} \int_{\substack{s_1,\dotsc s_{n-2}>s\\ s_1+\cdots +s_{n-2}\leq 1-3s}} (1-3s-s_1-\cdots -s_{n-2})^2 ds_1\cdots ds_{n-2}$$

$$=\frac{n!}{2!} \int_{\substack{s_1,\dotsc s_{n-3}>s\\ s_1+\cdots +s_{n-3}\leq 1-4s}} ds_1\cdots ds_{n-3} \int_s^{1-3s -s_1-\cdots -s_{n-3}} (1-3s-s_1-\cdots -s_{n-2})^2ds_{n-2}$$

$$=\frac{n!}{3!} \int_{\substack{s_1,\dotsc s_{n-3}>s\\ s_1+\cdots +s_{n-3}\leq 1-4s}} (1-4s-s_1-\cdots -s_{n-3})^3ds_1\cdots ds_{n-3}$$

$$=\cdots = \bigl(\, 1-(n+1)s\,\bigr)_+^n.$$

With

$$L_n:=\max_{1\leq i\leq n+1} S_i$$

we have

$$\bP[L_n>s]=\bP\left[\,\bigcup_{i=1}^{n+1}\bigl\lbrace\, S_i>s\,\bigr\rbrace\,\right]$$

(use inclusion-exclusion  exchangeability)

$$= \sum_{k=1}^{n+1}(-1)^{k+1}\binom{n+1}{k} \bP\bigl[\, S_1>s,\dotsc, S_k>s\,\bigr] =\sum_{k=1}^{n+1} \binom{n+1}{k}(1-ks)_+^n .$$

The last equality is sometime known as Whitworth  formula and it goes all the way back to 1897. We have

$$E_n=\bE[L_n]=\int_0^\infty \bP[L_n>s] ds =\sum_{k=1}^{n+1}(-1)^{k+1} \binom{n+1}{k}\int_0^\infty (1-ks)_+^n ds = \sum_{k=1}^{n+1}(-1)^{k+1} \binom{n+1}{k}\int_0^{1/k}(1-ks)^n ds$$

$$=\frac{1}{n+1} \sum_{k=1}^{n+1}(-1)^{k+1} \frac{1}{k} \binom{n+1}{k}=\sum_{k=1}^{n+1}(-1)^{k+1} \frac{1}{k^2} \binom{n}{k-1}.$$

Here  is  $E_n$ answers for  small $n$ (thank you MAPLE)

$$E_1=\frac{3}{4},\;\;E_2=\frac{11}{18},\;\;E_3=\frac{25}{48},\;\;E_4=\frac{137}{300},\;\; E_5=\frac{49}{120}.$$

It turn out that $L_n$ is highly concentrated around its mean. In the work of P. Levy I mentioned earlier he showed  that the random variables   $nL_n-\log n$ converges in probability   to a random  variable $X_\infty$   with probability  distribution  $\mu$ given by

$$\mu\bigl(\,(-\infty,x])= \exp\bigl(\,-e^{-x}\,\bigr),\;\;x\geq 0.$$

Moreover

$$\lim_{n\to \infty} \bE[\, nL_n-\log n\,]=\gamma=\bE[X_\infty],\;\;\lim_{n\to \infty}\mathrm{Var}[ nL_n\,]=\frac{\pi^2}{6}=\mathrm{Var}[X_\infty].$$

$$\frac{n}{\log n} L_n \to 1\;\; \mbox{a.s.}$$

Note that this shows that

$$E_n\sim\frac{\log n}{n}\;\; \mbox{as n\to \infty}.$$

I'll stop here, but you can learn more from this paper of Luc Devroye and its copious   list of references.