# Intuition behind Expectation of a sequence of independent, uniform random variables.

Let $\mathcal{U}_{1} , \mathcal{U}_{2}, \ldots$ be a sequence of independent, uniform random variables on $(0,1)$. Find $E[N]$ when $N = \min{\left\{n: \sum_{i=1}^{n} \mathcal{U}_{i} > 1 \right\}}$.

Let $N$ be the number of uniform random variables that need to be summed to exceed $1$.

$$N(x) = \min {\left\{n : \sum_{j=1}^{n} \mathcal{U}_{i} > x \right\}}$$ Let $m(x) = E[N(x)] = E \ [E \ [N(x)\mid \mathcal{U}_{1} ] \ ]= \int_{0}^{1} E \ [ N(x) \mid \mathcal{U}_{1} = y] \ dy$, where $$E \ [ N(x)\mid \mathcal{U}_{1} = y] = \begin{cases}1 & \text{if y>x}\\ 1 + E[(n-y)] & \text{if y \leq x}\end{cases}$$

I have a conceptual problem understanding the motivation behind the expression for the second case above. Would someone be willing to shed some light?

$$E \ [ N(x)\mid \mathcal{U}_{1} = y]=\begin{cases}1 & \text{if y>x}\\1 + m(x-y) & \text{if y \leq x}\end{cases} = 1 + \begin{cases}0 & \text{if y>x}\\ m(x-y) & \text{if y \leq x}\\\end{cases}$$ and so $$m(x) = 1 + \int_{0}^{x} m(x-y) \ dy \implies m'(x) = 0 + m(0) + \int_{0}^{x} m'(x-y) \ dy$$ We perform a change of variables with $u = x -y$ and $du = - dy$

$$m'(x)= m(0) - m(x-y) \ \bigg\vert_{0}^{X}$$ We substitute the initial value $m(0) = 1$ and simplify the antiderivative: $$m'(x) = m(0) - [m(0) - m(x)] \implies m(x) = C_{0} e^{x}$$ where $C_{0} = 1$, so $m(x) = e^{x}$ and $$m(1) = e$$

This result seems pretty neat since the $e$ comes in without any mention of the constant anywhere in the question. However, I can't appreciate it without understanding the origin of the $1 + m(x-y)$ term.

• I think there is some type it should be $\mathsf{E}(N(x)-Y)$ rather than $\mathsf{E}(n-y)$, if that is the case then the way you have defined $m(x)=\mathsf{E}(N(x)$, we have $N(x)-y=\min\{n:\sum_i U_i>x-y\}=N(x-y)$ hence $\mathsf{E}(N(x-y)=m(x-y)$ by definition of $m(x)$. – Shahid M Shah Dec 17 '15 at 3:20

If $\mathcal U_1=y\ge x$ then you are already there, so needed to "sum up" only $1$ random variable, so $$E[N(x) \mid \mathcal U_1=y>x]=1$$ Now, if $\mathcal U_1=y<x$ then you have "summed up" $1$ random variable and in order to reach $x$ you are still $x-y$ short. So, the expected number of r.v's to reach $x$ is $1+$ the expected number of r.v's to reach the remaining $x-y$. In your notation $1+m(x-y)$, or formally $$E[N(x)\mid \mathcal U_1=y<x]=1+m(x-y)$$