# Processes in continuous time¶

## The Poisson process¶

Definition (Poisson process) A Poisson process $$N_t$$ is a stochastic process with a continuous index set $$\mathcal{I} = [0, \infty)$$ which

1. Takes values in the range $$\{0, 1, 2, ... \}$$,

2. Is initially zero $$N_0 = 0$$,

3. Is nondecreasing $$s \leq t \implies N_s \leq N_t$$,

4. Satisfies the independence relation $$0 \leq s < t \implies N_s \perp \!\!\! \perp N_t - N_s$$.

5. For some number $$\lambda > 0$$, called the arrival rate, it satisfies

\begin{split}\begin{align} \mathbb{P}(N_{t + h} = n + 1 | N_t = n) &= \lambda h + o(h),\\ \mathbb{P}(N_{t + h} = n | N_t = n) &= 1 - \lambda h + o(h). \end{align}\end{split}

Theorem (Marginal distribution of a Poisson process) Let $$N_t$$ be a Poisson process with rate $$\lambda$$. Then for any $$t > 0$$

\begin{align} \mathbb{P}(N_t = k) = \frac{1}{k!} (\lambda t)^k e^{-\lambda t} & \text{ for } k = 0, 1, 2, ... . \end{align}

Below is the textbook proof for the above theorem.

Proof (a): Marginal distribution of a Poisson process

From the definition of a Poisson process, we have

\begin{split}\begin{align} \mathbb{P}(N_{t + h} = n) &= \sum_{k = 0}^\infty \mathbb{P}(N_{t + h} = n | N_t = k) \mathbb{P}(N_t = k)\\ \\ &= \mathbb{P}(N_{t + h} = n | N_t = n) \mathbb{P}(N_t = n) + \mathbb{P}(N_{t + h} = n | N_t = n - 1) \mathbb{P}(N_t = n - 1) \\ \\ &= \left(1 - \lambda h\right) \mathbb{P}(N_t = n) + \lambda h \mathbb{P}(N_t = n - 1) + o(h). \end{align}\end{split}

Using the notation $$p_k(t) = \mathbb{P}(N_t = k)$$, we obtain the differential equation

\begin{align} p_n'(t) = \lambda p_{n - 1}(t) - \lambda p_n(t), \end{align}

for $$n \geq 1$$, while for $$n = 0$$ we obtain

\begin{align} p_0'(t) = - \lambda p_0(t). \end{align}

Considering the generating function of $$N_t$$

\begin{align} G(s, t) = \sum^\infty_{k = 0} p_k(t)s^k, \end{align}

and taking sums over both sides of the differential equation, we obtain

\begin{align} \sum^\infty_{k = 1} p_k'(t)s^k &= \lambda \sum^\infty_{k = 1} p_{k - 1}(t)s^k - \lambda \sum^\infty_{k = 1} p_k(t)s^k. \end{align}

Adding each side of the equation $$p_0'(t) = - \lambda p_0(t)$$ to each side of the above equations, we obtain

\begin{align} \sum^\infty_{k = 0} p_k'(t)s^k &= \lambda \sum^\infty_{k = 1} p_{k - 1}(t)s^k - \lambda \sum^\infty_{k = 0} p_k(t)s^k, \end{align}

and arrive at the differential equation

\begin{align} \frac{\partial G}{\partial t} = \lambda s G - \lambda G. \end{align}

Integrating the differential equation with respect to $$t$$ we obtain

\begin{align} \log G = \lambda t (s - 1) + A(s). \end{align}

The boundary condition

\begin{align} G(s, 0) = \sum^\infty_{k = 0} p_k(0) s^k = 1. \end{align}

implies $$\log G(s, 0) = 0 = A(s)$$, so

\begin{align} G(s, t) = e^{\lambda t (s - 1)} = \sum_{k = 0}^\infty \left[\frac{1}{k!} (\lambda t)^k e^{-\lambda t} s^k\right] . \end{align}

This, together with the uniqueness theorem for moments implies that

\begin{align} p_n(t) = \frac{1}{n!} (\lambda t)^n e^{- \lambda t}. \end{align}

An alternative proof that avoids differential equations is given below.

Proof (b): Marginal distribution of a Poisson process

Consider the sequence of random variables $$Z_1(t), Z_2(t),~...$$ defined by

\begin{align} Z_N(t) = \sum^N_{n = 1} B_{N, n}, \end{align}

where $$B_{N, n}$$ are i.i.d. Bernoulli-distributed random variables with parameter $$p = \lambda \frac{t}{N}$$. The first part of this proof shows that the sequence $$Z_N(t)$$ converges in distribution to $$N_t$$, and the second shows that the limiting distribution is Poisson with parameter $$\lambda t$$.

First, we show that the sequence $$Z_N(t)$$ converges in distribution to $$N_t$$. By the definition of $$Z(t)$$, we have

\begin{split}\begin{align} \mathbb{P}(Z_N = k) &= \sum_{\mathcal{u}_N \in \mathcal{B}_N} \prod_{n = 1}^N \mathbb{P}(B_{N, n} = b_{N, n}) \\ &= \sum_{\mathcal{u}_N \in \mathcal{B}_N} \prod_{n = 1}^N \left(\lambda \frac{t}{N}\right)^{b_{N, n}} \left(1 - \lambda \frac{t}{N}\right)^{1 - b_{N, n}} \end{align}\end{split}

where the sum is over $$\mathcal{B}_N$$, the set of all binary $$N$$-tuples $$\mathcal{u}_N = (b_{N, 1}, b_{N, 2}, ..., b_{N, N})$$ such that $$b_{N, n} \in \{0, 1\}$$ and $$\sum_{n = 1}^N b_{N, n} = k$$. Then from the definition of $$N_t$$

\begin{split}\begin{align} \mathbb{P}(N_t = k) &= \sum_{\mathcal{v}_N \in \mathcal{C}_N} \prod_{n = 1}^N \mathbb{P}(N_{t + nh} = c_{N, n} | N_{(n - 1)h} = c_{N, n - 1}) \\ &= \sum_{\mathcal{v}_N \in \mathcal{C}_N} \prod_{n = 1}^N \left(\lambda \frac{t}{N} + o\left(\frac{t}{N}\right)\right)^{c_{N, n} - c_{N, n - 1}} \left(1 - \lambda \frac{t}{N} + o\left(\frac{t}{N}\right)\right)^{1 - (c_{N, n} - c_{N, n - 1})} \end{align}\end{split}

where the sum is over $$\mathcal{C}_N$$, the set of all binary $$N$$-tuples $$\mathcal{v}_N = (c_{N, 1}, c_{N, 2}, ..., c_{N, N})$$ such that $$c_{N, 1} \in \{0, 1\}$$, $$c_{N, N} = k$$ and $$c_{N, n + 1} - c_{N, n} \in \{0, 1\}$$ for $$n > 1$$. Noting the correspondence between the sets $$\mathcal{B}_N$$ and $$\mathcal{C}_N$$ and their elements, and taking the limit $$N \to \infty$$ we obtain

\begin{align} \lim_{N \to \infty} \mathbb{P}(Z_N = k) = \mathbb{P}(N_t = k). \end{align}

We now show that $$Z(t) = \lim_{N \to \infty} Z_N(t)$$ is Poisson-distrbuted. The characteristic function of $$B_{N, n}$$ is

\begin{align} \phi_{B_{N, n}}(u) = \mathbb{E}\left[e^{iuB_{N, n}}\right] = 1 - \lambda \frac{t}{N} + \lambda \frac{t}{N} e^{iu} + o\left(\frac{1}{N}\right). \end{align}

Using the independence of the $$B_{N, n}$$ variables and the independence property of the characteristic function we obtain

\begin{align} \phi_{Z_N}(u) = \left[1 - \lambda \frac{t}{N} + \lambda \frac{t}{N} e^{iu} + o\left(\frac{t}{N}\right) \right]^{\lambda t N}. \end{align}

As $$N \to \infty$$, the sequence of characteristic functions converges to the limit

\begin{align} \phi_{Z_N}(u) \to e^{\lambda t (e^{iu} - 1)} \end{align}

which is the characteristic function of a random variable $$Z(t)$$ that is Poisson-distributed with parameter $$\lambda t$$, so $$N_t$$ is also Poisson-distributed with parameter $$\lambda t$$.

## Arrival and Inter-arrival times¶

Definition (Arrival and inter-arrival times of the Poisson process) Let $$N_t$$ be a Poisson process with rate $$\lambda$$. The arrival times $$T_0, T_1, ...$$ of $$N_t$$ are defined as $$T_0 = 0$$ and

\begin{align} T_k = \inf \{t : N_t = k\}, \text{ for } k = 1, 2, ... . \end{align}

The inter-arrival times $$X_1, X_2, ...$$ of the process are defined as

\begin{align} X_k = T_k - T_{k - 1}, \text{ for } k = 1, 2, ... . \end{align}

Theorem (Inter-arrival times of the Poisson process) Let $$N_t$$ be a Poisson process with rate $$\lambda$$. The inter-arrival times $$X_1, X_2, ...$$ are independent, each having the exponential distribution with parameter $$\lambda$$.

Below is an adapted version of the proof in Grimmett and Stirzaker, Probability and Random Processes.[GS01]

Proof: Inter-arrival times of the Poisson process

Let $$N_t$$ be a Poisson process with parameter $$\lambda$$ and inter-arrival times $$X_1, X_2, ...$$. The event $$\{X_1 > t\}$$ has probability

\begin{split}\begin{align} \mathbb{P}(X_1 > t) &= \lim_{h \to \infty} (1 - \lambda h + o(h))^{\frac{t}{h}} \\ &= e^{-\lambda t}, \end{align}\end{split}

and $$X_1$$ is therefore exponentially distributed with parameter $$\lambda$$. Now considering $$X_2$$, we have

\begin{split}\begin{align} \mathbb{P}(X_2 > t | X_1 = t_1) &= \mathbb{P}(\text{no arrivals in } (t_1, t_1 + t] | X_1 = t_1)\\ &= \mathbb{P}(\text{no arrivals in } (t_1, t_1 + t])\\ &= \lim_{h \to \infty} (1 - \lambda h + o(h))^{\frac{t}{h}} \\ &= e^{-\lambda t}, \end{align}\end{split}

where to go from the first to the second line we used the following fact. The event $$\{X_1 = t_1\}$$ is determined by the number of arrivals occuring during $$[0, t_1]$$ whereas the event $$\{\text{no arrivals in } (t_1, t_1 + t]\}$$ is determined by the number of arrivals during $$(t_1, t_1 + t]$$. By the independence property of the Poisson process, the numbers of arrivals during these two intervals are independent and therefore the two events are also independent. Therefore $$X_1$$ and $$X_2$$ are independent and $$X_2$$ is also exponentially distributed with parameter $$\lambda$$. Proceeding recursively yields the result.

## Lack of memory property¶

Definition (Lack-of-memory property) A positive random variable $$X$$ is said to have the lack-of-memory property if

\begin{align} \mathbb{P}(X > u + v | X > u) = \mathbb{P}(X > v). \end{align}

Theorem (Lack-of-memory $$\iff$$ exponentially distributed) The continuous random variable $$X$$ has the lack of memory property if and only if it is exponentially distributed.

Proof: Lack-of-memory $$\iff$$ exponentially distributed

To see that the exponential distribution has the lack-of-memory property, let $$X$$ be exponentially distributed with parameter $$\lambda$$ and consider

\begin{split}\begin{align} \mathbb{P}(X > u + v | X > u) &= \frac{\mathbb{P}(X > u + v)}{\mathbb{P}(X > u)} \\ &= \frac{e^{-\lambda (u + v)}}{e^{-\lambda u}} \\ &= e^{-\lambda v} \\ &= \mathbb{P}(X > v). \end{align}\end{split}

To show that a continuous random variable $$X$$ which has the lack-of-memory property, must be exponentially distributed, consider

\begin{align} \mathbb{P}(X > u + v | X > u) = \mathbb{P}(X > v) \implies \mathbb{P}(X > u + v) = \mathbb{P}(X > u)\mathbb{P}(X > v) \end{align}

Therefore, the continuous function $$G(\cdot) = \mathbb{P}(X > \cdot)$$ satisfies

\begin{align} G(u + v) = G(u)G(v), \text{ for } u, v \geq 0. \end{align}

It follows that for any non-zero integer $$n$$

\begin{align} G(n) = G(1)^n, \end{align}

and similarly, for any rational $$u = \frac{a}{b}$$ where $$a, b$$ are integers, we have

\begin{align} G\left(\frac{a}{b}\right)^b &= G\left(a\right) = G\left(1\right)^a, \end{align}

from which it follows that $$G(u) = G(1)^u$$. This relation holds for all rational $$u$$, and by the continuity of $$G$$ it also holds for all real numbers. Finally, since $$G(u)$$ is monotonic decreasing, it must be the case that $$0 < G(1) < 1$$, so defining $$\lambda = - \log G(1)$$ we arrive at

\begin{align} G(u) = \mathbb{P}(X > u) = e^{-\lambda t}, \text{ for some } \lambda > 0, \end{align}

so $$X$$ must be exponentially distributed with some parameter $$\lambda > 0$$.

## Simple birth process¶

Definition (Simple birth process) A simple birth process, $$M_t$$, is a stochastic process describing the evolution of a population, such that

1. The initial population is an integer $$I$$, $$M_0 = I$$,

2. There exists a number $$\lambda > 0$$ called the birth rate, such that during the interval $$(t, t + h]$$, each organism of the population has a probability of dividing into two organisms, equal to $$\lambda h + o(h)$$ and a probability of not dividing equal to $$1 - \lambda h + o(h)$$,

3. For each organism at time $$t$$, its future divisions are independent of its past divisions, as well as the divisions of all other organisms present at time $$t$$.

Theorem (Population distribution of the simple birth process) Let $$M_t$$ be a simple birth process with rate $$\lambda$$, and initial population $$M_0 = I$$. Then

\begin{align} \mathbb{P}(M_t = k) = {k - 1 \choose I - 1} e^{-I\lambda t}(1 - e^{-\lambda t})^{k - I}, \text{ for } k = I, I + 1, ... . \end{align}

Proof: Population distribution of the simple birth process

Let $$p_k(t) = \mathbb{P}(M_t = k)$$ and consider

\begin{split}\begin{align} \mathbb{P}(M_t = k) &= \sum_{i = 0}^\infty \mathbb{P}(M_t = k | M_t = i) \mathbb{P}(M_t = i)\\ &= \left[1 - \lambda k h + o(h)\right]\mathbb{P}(M_t = k | M_t = k) + \left[\lambda (k - 1) h + o(h)\right]\mathbb{P}(M_t = k | M_t = k - 1) + o(h). \end{align}\end{split}

Subtracting $$p_k(t)$$ from both sides and taking the $$h \to 0$$ limit, we obtain

\begin{align} p_k'(t) = \lambda (k - 1)p_{k - 1}(t) - \lambda k p_k(t) & \text{ for } k = I, I + 1, ... , \end{align}

with $$p_{I - 1}(t) = 0$$ and boundary conditions

\begin{split}\begin{align} p_k(0) = \begin{cases} 1 & \text{ if } k = I, \\ 0 & \text{ if } k \neq I. \end{cases} \end{align}\end{split}

Therefore for $$k = I$$

\begin{align} p_I'(t) = - \lambda I p_I(t) \implies p_I(t) = e^{-\lambda I t}. \end{align}

## Birth and death process¶

Definition (Birth and death process) A birth and death process, $$L_t$$, is a stochastic process describing the evolution of a population, such that

1. The initial population is an integer $$I$$, $$M_0 = I$$,

2. There exists a number $$\lambda > 0$$ called the birth rate, such that during the interval $$(t, t + h]$$, each organism of the population has a probability of dividing once into two organisms, equal to $$\lambda h + o(h)$$ and a probability of dividing more than once is $$o(h)$$,

3. There exists a number $$\mu > 0$$ called the death rate, such that during the interval $$(t, t + h]$$ each organism has a probability $$\mu h + o(h)$$ of dying,

4. For each organism at time $$t$$, its future activity (divisions or death) is independent of its past activity, as well as the activities of all other organisms present at time $$t$$.

Theorem (PGF of birth and death process) Let $$L_t$$ be a birth and death process with birth rate $$\lambda$$ and death rate $$\mu > 0$$, and initial population $$L_0 = I$$. Then its pgf is

\begin{split}\begin{align} \mathbb{E}(s^{L_t}) = \begin{cases} \left(\frac{\lambda t (1 - s) + s}{\lambda t (1 - s) + 1}\right)^I & \text{ if } \mu = \lambda \\ \left(\frac{\mu (1 - s) - (\mu - \lambda s)e^{t(\mu - \lambda)}}{\lambda (1 - s) - (\mu - \lambda s)e^{t(\mu - \lambda)}}\right)^I & \text{ if } \mu \neq \lambda. \end{cases} \end{align}\end{split}

Theorem (Extinction of birth and death process) Let $$L_t$$ be a birth and death process with birth rate $$\lambda$$ and death rate $$\mu > 0$$, and initial population $$L_0 = I$$. The probability $$e(t) = \mathbb{P}(L_t = 0)$$ that the probability is extinct by time $$t$$ tends to

\begin{split}\begin{align} e(t) \to \begin{cases} 1 & \text{ if } \lambda \leq \mu, \\ \left(\frac{\mu}{\lambda}\right)^I & \text{ if } \lambda > \mu. \end{cases} \end{align}\end{split}

Proof: Extinction of birth and death process

Let $$L_t$$ be a birth and death process with birth rate $$\lambda$$ and death rate $$\mu > 0$$, and initial population $$L_0 = I$$. If the pgf at time $$t$$ is $$G(s, t)$$, then $$G(0, t) = \mathbb{P}(L_t = 0)$$ and

\begin{split}\begin{align} G(0, t) = \begin{cases} \left(\frac{\lambda t + s}{\lambda t + 1}\right)^I & \text{ if } \mu = \lambda \\ \left(\frac{\mu - \mu e^{t(\mu - \lambda)}}{\lambda - \mu e^{t(\mu - \lambda)}}\right)^I & \text{ if } \mu \neq \lambda. \end{cases} \end{align}\end{split}

Letting $$t \to \infty$$ we arrive at the result.

## First come, first served queue¶

Definition (First come, first served queue) A first come, first served queue $$Q_t$$, is a stochastic process describing the evolution of a system where customers arrive, are queued and served, such that

1. The initial number of customers in the queue is an integer $$I$$, $$Q_0 = I$$,

2. Customers arrive in the manner of a Poisson process with rate $$\lambda > 0$$.

3. The time taken to serve each customer is exponentially distributed with parameter $$\mu > 0$$, and service times are independent of each other.

4. The inter-arrival times and service times are all independent variables.

Theorem (Steady state of the first come, first served queue) Let $$Q_t$$ be a first come, first served queue with arrival rate $$\lambda$$ and service rate $$\mu > 0$$. If $$\lambda < \mu$$, the queue has a unique steady-state distribution given by

\begin{align} \pi_k = \left(1 - \frac{\lambda}{\mu}\right) \left(\frac{\lambda}{\mu}\right)^k, \text{ for } k = 0, 1, 2, ... , \end{align}

independent of $$Q_0$$. If $$\lambda \geq \mu$$, there is no steady-state distribution.

Proof: Steady state of the first come, first served queue

Let $$Q_t$$ be a first come, first served queue with arrival rate $$\lambda$$ and service rate $$\mu > 0$$, and write $$p_k(t) = \mathbb{P}(Q_t = k)$$. Then

\begin{split}\begin{align} p_k(t) = \begin{cases} \lambda p_{k - 1}(t) - (\lambda + \mu) p_k(t) + \mu p_{k + 1}(t) & \text{ if } k \neq 0 \\ - \lambda p_0(t) + \mu p_1(t) & \text{ if } k = 0. \end{cases} \end{align}\end{split}

Now assume there exists a steady state

\begin{align} \pi_k = \lim_{t \to \infty} p_k(t), \end{align}

it will satisfy $$p_k'(t) = 0$$, so

\begin{split}\begin{align} \lambda \pi_{k - 1} - (\lambda + \mu) \pi_k + \mu \pi_{k + 1} &= 0 \\ - \lambda \pi_0 + \mu \pi_1 &= 0. \end{align}\end{split}

Therefore $$\pi_1 = \rho \pi_0$$ where $$\rho = \frac{\lambda}{\mu}$$. Continuing recursively

\begin{split}\begin{align} \pi_2 &= (1 + \rho) \pi_1 - \rho \pi_0 \\ &= \rho^2 \pi_0, \end{align}\end{split}

arriving at $$\pi_k = \rho^k \pi_0$$. If $$\rho < 1$$ then

\begin{align} \sum^\infty_{k = 0} \pi_k = 1 \iff \pi_0 = 1 - \rho, \end{align}

arriving at the result

\begin{align} \pi_k = \left(1 - \frac{\lambda}{\mu}\right) \left(\frac{\lambda}{\mu}\right)^k. \end{align}

On the other hand, if $$\rho \geq 1$$ $$\pi_k$$ cannot be a normalised distribution, contradicting our assumption that a steady state exists.

## References¶

GS01

G.R. Grimmett and D.R. Stirzaker. Probability and random processes. Number 391. Oxford university press, 2001.