# Transforming sum of n exponential distribution to a Poisson distribution

Let $$X_1,...,X_n$$ be i.i.d exponential random variable with mean $$\lambda$$

$$S=X_1+...+X_n$$

So by finding the mgf of S, we get that $$S \sim \operatorname{Gamma}(n,\lambda)$$

The problem I am stuck at is how can we show:

for a given positive t, $$N = max\{n:n\geq 1, S \leq t\}$$ has a Poisson distribution

how should I start?

Here's an unconventional but for me more insightful way to do this: If $$X_1, X_2, X_3, \cdots \stackrel{\text{iid}}{\sim} \text{Exp}(\lambda)$$ then denote $$S_k = X_1 + \cdots + X_k$$, $$k \geq 1$$ to be the sequence of partial sums. I claim that for any fixed $$n \geq 1$$, the conditional distribution $$(S_1, \cdots, S_n) | S_{n+1} = s$$ is equal in distribution with the order statistics vector $$(U_{(1)}, \cdots, U_{(n)})$$ of the sample $$U_1, \cdots, U_{n} \stackrel{\text{iid}}{\sim} \text{Unif}(0, s)$$.

To prove this, define a linear transformation $$T(x_1, \cdots, x_{n+1}) = (x_1, x_1+x_2, \cdots, x_1+\cdots+x_{n+1})$$ for all $$\mathbf{x} \in (0, \infty)^{n+1}$$. This has range $$\{\mathbf{s} \in \Bbb R^n : 0 < s_1 < \cdots < s_{n+1}\}$$ and is invertible on the range, with inverse given by $$T^{-1}(s_1, \cdots, s_{n+1}) = (s_1, s_2 - s_1, \cdots, s_{n+1} - s_n)$$. Clearly $$\det(T) = 1$$ as the matrix of $$T$$ is upper triangular with $$1$$'s along the diagonal. By the change of density formula,

\begin{align}f_{S_1, \cdots, S_{n+1}}(s_1, s_2, \cdots, s_{n+1}) & = f_{X_1, \cdots, X_{n+1}}(s_1, s_2 - s_1, \cdots, s_{n+1} - s_n) \\ &= \lambda e^{-\lambda s_1} \lambda e^{-\lambda (s_2 - s_1)} \cdots \lambda e^{-\lambda (s_{n+1} - s_n)} \mathbf{1}_{(0 < s_1 < \cdots < s_{n+1})} \\ &= \lambda^{n+1}e^{-\lambda s_{n+1}} \mathbf{1}_{(0 < s_1 < \cdots < s_{n+1})} \end{align}

$$S_{n+1}$$ is distributed as $$\text{Gamma}(n+1, \lambda)$$, hence $$f_{S_{n+1}}(s) = \lambda^{n+1} s^n e^{-\lambda s}/n!$$, so the conditional pdf of the conditional distribution $$(S_1, \cdots, S_n)|S_{n+1} = s$$ is given by

\begin{align}f_{S_1 \cdots S_n | S_{n+1}}(s_1, \cdots, s_n | s) = \frac{f_{S_1, \cdots, S_{n+1}}(s_1, \cdots, s_n, s)}{f_{S_{n+1}}(s)} &= \frac{\lambda^{n+1} e^{-\lambda s}}{\lambda^{n+1}s^ne^{-\lambda s}/n!} \mathbf{1}_{(0

which is precisely the pdf of $$(U_{(1)}, \cdots, U_{(n)})$$, as promised. There's some intuition behind this: the exponential distribution with parameter $$\lambda$$ is roughly speaking the time taken for a continuous process to change state: this is because if $$X_n \sim\text{Geo}(\lambda/n)$$, which is the time taken for a discrete process (tossing a coin with success probability $$\lambda/n$$) to change state (i.e., the first success to occur), then $$X_n/n$$ converge in distribution to $$X \sim \text{Exp}(\lambda)$$ (so think of tossing a coin very fast with very small success probability). Given this interpretation, if $$X_1, \cdots, X_{n+1}$$ are iid $$\text{Exp}(\lambda)$$, knowing that $$S_{n+1} = s$$ (it took $$s$$ amount of time for the process to change state $$n+1$$ times) doesn't specify anything about the time taken for the process to change state $$k$$ times for $$1 \leq k \leq n$$, except that they are ordered.

$$N$$ as above is a sort of stopping time of the continuous process, which takes values in $$\Bbb N \cup \{0\}$$, where after $$N+1$$ changes in state the total time taken exceeds $$t$$. $$\Bbb P(N = 0)$$ can be computed by hand to be $$\Bbb P(S_1 > t) = e^{-\lambda t}$$. In general by law of total probability,

\begin{align} \Bbb P(N = n) = \Bbb P(S_n \leq t, S_{n+1} > t) &= \int_t^\infty \Bbb P(S_n \leq t|S_{n+1} = u)f_{S_{n+1}}(u) du \\ &= \int_t^\infty \Bbb P(U_{(n)} \leq t) f_{S_{n+1}}(u) du\end{align}

Where $$U_{(n)}$$ is the $$n$$-th maximum of a sample $$(U_1, \cdots, U_n)$$ of size $$n$$ from $$\text{Unif}(0, u)$$. As $$0 < t < u$$, the distribution function is $$\Bbb P(U_{(n)} \leq t) = (t/u)^n$$, and as $$S_{n+1}$$ is distributed as $$\text{Gamma}(n+1, \lambda)$$, we have $$f_{S_{n+1}}(u) = \lambda^{n+1} u^n e^{-\lambda u}/n!$$. Plugging all of this in, we get for all $$n \geq 1$$,

\begin{align}\Bbb P(N = n) = \int_t^\infty \left ( \frac{t}{u}\right )^n \frac{\lambda^{n+1}}{n!} u^n e^{-\lambda u} du &= \frac{\lambda^{n+1}t^n}{n!}\int_t^\infty e^{-\lambda u} du \\ &= \frac{\lambda^{n+1} t^n}{n!} \frac{e^{-\lambda t}}{\lambda} = \frac{(\lambda t)^n}{n!}e^{-\lambda t}\end{align}

Therefore, $$N \sim \text{Poi}(\lambda t)$$ as the pmf's match. For what it's worth, a more conventional way to do it is to show by hand that if $$X \sim \text{Gamma}(n+1, \lambda)$$ and $$Y \sim \text{Poi}(\lambda t)$$ then $$\Bbb P(X > t) = \Bbb P(Y \leq n)$$, i.e., the survival function of $$X$$ is the cdf of $$Y$$. This is just an exercise in integration-by-parts. Then

\begin{align}\Bbb P(N = n) = \Bbb P(S_n \leq t, S_{n+1} > t) &= \Bbb P(S_{n+1} > t) - \Bbb P(S_n > t) \\ &= \Bbb P(Y \leq n) - \Bbb P(Y \leq {n-1}) \\ &= \Bbb P(Y = n)\end{align}

Which concludes $$N \stackrel{d}{=} Y$$ is a Poisson distribution.