
## 20. Chains Subordinate to the Poisson Process

### Basic Theory

#### Introduction

Recall that the standard Poisson process with rate parameter $$r \in (0, \infty)$$ involves three interrelated stochastic processes. First the sequence of interarrival times $$\bs{T} = (T_1, T_2, \ldots)$$ is independent, and each variable has the exponential distribution with parameter $$r$$. Next, the sequence of arrival times $$\bs{\tau} = (\tau_0, \tau_1, \ldots)$$ is the partial sum sequence associated with the interrival sequence $$\bs{T}$$: $\tau_n = \sum_{i=1}^n T_i, \quad n \in \N$ For $$n \in \N_+$$, the arrival time $$\tau_n$$ has the gamma distribution with parameters $$n$$ and $$r$$. Finally, the Poisson counting process $$\bs{N} = \{N_t: t \in [0, \infty)\}$$ is defined by $N_t = \max\{n \in \N: \tau_n \le t\}, \quad t \in [0, \infty)$ so that $$N_t$$ is the number of arrivals in $$(0, t]$$ for $$t \in [0, \infty)$$. The counting variable $$N_t$$ has the Poisson distribution with parameter $$r t$$ for $$t \in [0, \infty)$$. The counting process $$\bs{N}$$ and the arrival time process $$\bs{\tau}$$ are inverses in the sense that $$\tau_n \le t$$ if and only if $$N_t \ge n$$ for $$t \in [0, \infty)$$ and $$n \in \N$$. The Poisson counting process can be viewed as a continuous-time Markov chain.

Suppose that $$X_0$$ takes values in $$\N$$ and is independent of $$\bs{N}$$. Define $$X_t = X_0 + N_t$$ for $$t \in [0, \infty)$$. Then $$\bs{X} = \{X_t: t \in [0, \infty)\}$$ is a continuous-time Markov chain on $$\N$$ with exponential parameter function given by $$\lambda(x) = r$$ for $$x \in \N$$ and jump transition matrix $$Q$$ given by $$Q(x, x + 1) = 1$$ for $$x \in S$$.

Proof:

This follows directly from the basic structure of a continuous-time Markov chain. Given $$X_t = x$$, the holding time in state $$x \in \N$$ is exponential with parameter $$r$$, and the next state is deterministically $$x + 1$$. Note that the addition of the variable $$X_0$$ is just to allow us the freedom of arbitrary initial distributions on the state space, as is routine with Markov processes.

Note that the Poisson process, viewed as a Markov chain is a pure birth chain. Clearly we can generalize this continuous-time Markov chain in a simple way by allowing a general embedded jump chain.

Suppose that $$\bs{X} = \{X_t: t \in [0, \infty)\}$$ is a Markov chain with (countable) state space $$S$$, and with constant exponential parameter $$\lambda(x) = r \in (0, \infty)$$ for $$x \in S$$, and jump transition matrix $$Q$$. Then $$\bs{X}$$ is said to be subordinate to the Poisson process with rate parameter $$r$$.

1. The transition times $$(\tau_1, \tau_2, \ldots)$$ are the arrival times of the Poisson process with rate $$r$$.
2. The inter-transition times $$(\tau_1, \tau_2 - \tau_1, \ldots)$$ are the inter-arrival times of the Poisson process with rate $$r$$ (independent, and each with the exponential distribution with rate $$r$$).
3. $$\bs{N} = \{N_t: t \in [0, \infty)\}$$ is the Poisson counting process, where $$N_t$$ is the number of transitions in (0, t] for $$t \in [0, \infty)$$.
4. The Poisson process and the jump chain $$\bs{Y} = (Y_0, Y_1, \ldots)$$ are independent, and $$X_t = Y_{N_t}$$ for $$t \in [0, \infty)$$.
Proof:

These results all follow from the basic structure of a continuous-time Markov chain.

Since all states are stable, note that we must have $$Q(x, x) = 0$$ for $$x \in S$$. Note also that for $$x, \, y \in S$$ with $$x \ne y$$, the exponential rate parameter for the transition from $$x$$ to $$y$$ is $$\mu(x, y) = r Q(x, y)$$. Conversely suppose that $$\mu: S^2 \to (0, \infty)$$ satisfies $$\mu(x, x) = 0$$ and $$\sum_{y \in S} \mu(x, y) = r$$ for every $$x \in S$$. Then the Markov chain with transition rates given by $$\mu$$ is subordinate to the Poisson process with rate $$r$$. It's easy to construct a Markov chain subordinate to the Poisson process.

Suppose that $$\bs N = \{N_t: t \in [0, \infty)\}$$ is a Poisson counting process with rate $$r \in (0, \infty)$$ and that $$\bs Y = \{Y_n: n \in \N\}$$ is a discrete-time Markov chain on $$S$$, independent of $$\bs N$$, whose transition matrix satisfies $$Q(x, x) = 0$$ for every $$x \in S$$. Let $$X_t = Y_{N_t}$$ for $$t \in [0, \infty)$$. Then $$\bs X = \{X_t: t \in [0, \infty)\}$$ is a continuous-time Markov chain subordinate to the Poisson process.

#### Generator and Transition Matrices

Next let's find the generator matrix and the transition semigroup. Suppose again that $$\bs{X} = \{X_t: t \in [0, \infty)\}$$ is a continuous-time Markov chain on $$S$$ subordinate to the Poisson process with rate $$r \in (0, \infty)$$ and with jump transition matrix $$Q$$. As usual, let $$\bs P = \{P_t: t \in [0, \infty)\}$$ denote the transition semigroup and $$G$$ the infinitesimal generator.

The generator matrix $$G$$ of $$\bs{X}$$ is $$G = r (Q - I)$$. Hence for $$t \in [0, \infty)$$

1. The Kolmogorov backward equation is $$P^\prime_t = r (Q - I) P_t$$
2. The Kolmogorov forward equation is $$P^\prime_t = r P_t (Q - I)$$
Proof:

This follows directly from the general theory since $$G(x, x) = -\lambda(x) = -r$$ for $$x \in S$$ and $$G(x, y) = \lambda(x) Q(x, y) = r Q(x, y)$$ for distinct $$x, \, y \in S$$.

There are several ways to find the transition semigroup $$\bs{P} = \{P_t: t \in [0, \infty)\}$$. The best way is a probabilistic argument using the underlying Poisson process.

For $$t \in [0, \infty)$$, the transition matrix $$P_t$$ is given by $P_t = \sum_{n=0}^\infty e^{-r t} \frac{(r t)^n}{n!} Q^n$

Proof from the underlying Poisson process

Let $$N_t$$ denote the number of transitions in $$(0, t]$$ for $$t \in [0, \infty)$$, so that $$\bs{N} = \{N_t: t \in [0, \infty)\}$$ is the Poisson counting process. Let $$\bs{Y} = (Y_0, Y_1, \ldots)$$ denote the jump chain, with transition matrix $$Q$$. Then $$\bs{N}$$ and $$\bs{Y}$$ are independent, and $$X_t = Y_{N_t}$$ for $$t \in [0, \infty)$$. Conditioning we have \begin{align*} P_t(x, y) & = \P(X_t = y \mid X_0 = x) = \P\left(Y_{N_t} = y \mid Y_0 = x\right) \\ & = \sum_{n=0}^\infty \P\left(Y_{N_t} = y \mid N_t = n, Y_0 = y\right) \P(N_t = n \mid Y_0 = y) \\ & = \sum_{n=0}^\infty \P(Y_n = y \mid Y_0 = x) \P(N_t = n) = \sum_{n=0}^\infty e^{-r t} \frac{(r t)^n}{n!} Q^n(x, y) \end{align*}

Proof using the generator matrix

Note first that for $$n \in \N$$, $G^n = [r (Q - I)]^n = r^n \sum_{k = 0}^n \binom{n}{k}(-1)^{n-k} Q^k$ Hence \begin{align*} P_t & = e^{t G} = \sum_{n=0}^\infty \frac{t^n}{n!} G^n = \sum_{n=0}^\infty \frac{t^n}{n!} r^n \sum_{k=0}^\infty \binom{n}{k} (-1)^{n-k}Q^k \\ & = \sum_{n=0}^\infty \sum_{k=0}^n \frac{(r t)^n}{k! (n - k)!} (-1)^{n-k} Q^k = \sum_{k=0}^\infty \sum_{n=k}^\infty \frac{(r t)^n}{k! (n - k)!} (-1)^{n-k} Q^k \\ & = \sum_{k=0}^\infty \frac{(r t)^k}{k!} Q^k \sum_{n=k}^\infty \frac{1}{(n - k)!}(- r t)^{n-k} = \sum_{k=0}^\infty e^{-r t} \frac{(r t)^k}{k!} Q^k \end{align*}

#### Potential Matrices

Next let's find the potential matrices. As with the transition matrices, we can do this in (at least) two different ways.

Suppose again that $$\bs{X} = \{X_t: t \in [0, \infty)\}$$ is a continuous-time Markov chain on $$S$$ subordinate to the Poisson process with rate $$r \in (0, \infty)$$ and with jump transition matrix $$Q$$. For $$\alpha \in (0, \infty)$$, the potential matrix $$U_\alpha$$ of $$\bs{X}$$ is $U_\alpha = \frac{1}{\alpha + r} \sum_{n=0}^\infty \left(\frac{r}{\alpha + r}\right)^n Q^n$

Proof from the definition:

Using the previous result, \begin{align*} U_\alpha(x, y) & = \int_0^\infty e^{-\alpha t} P_t(x, y) \, dt = \int_0^\infty e^{-\alpha t} \sum_{n=0}^\infty e^{-r t} \frac{(r t)^n}{n!} Q^n(x, y) \, dt \\ & = \sum_{n=0}^\infty Q^n(x, y) \frac{r^n}{n!} \int_0^\infty e^{-(r + \alpha) t} t^n dt \end{align*} The interchange of sum and integral is justified since the terms are nonnegative. Using the change of variables $$s = (r + \alpha) t$$ gives $U_\alpha(x, y) = \frac{1}{\alpha + r} \sum_{n=0}^\infty \left(\frac{r}{\alpha + r}\right)^n \frac{1}{n!} Q^n(x, y) \int_0^\infty e^{-s t} s^n \, ds$ The last integral is $$n!$$.

Proof using the generator:

From the result above, $\alpha I - G = \alpha I - r (Q - I) = (\alpha + r) I - r Q = (\alpha + r)\left(I - \frac{r}{\alpha + r} Q\right)$ Since $$\left\| \frac{r}{\alpha + r} Q \right\| = \frac{r}{\alpha + r} \lt 1$$ we have $(\alpha I - G)^{-1} = \frac{1}{\alpha + r}\left(I - \frac{r}{\alpha + r} Q\right)^{-1} = \frac{1}{\alpha + r} \sum_{n=0}^\infty \left(\frac{r}{\alpha + r}\right)^n Q^n$

Recall that for $$p \in (0, 1)$$, the $$p$$-potential matrix of the jump chain $$\bs{Y}$$ is $$R_p = \sum_{n=0}^\infty p^n Q^n$$. Hence we have the following nice relationship between the potential matrix of $$\bs{X}$$ and the potential matrix of $$\bs{Y}$$: $U_\alpha = \frac{1}{\alpha + r} R_{r / (\alpha + r)}$ Next recall that $$\alpha U_\alpha(x, \cdot)$$ is the probability density function of $$X_T$$ given $$X_0 = x$$, where $$T$$ has the exponential distribution with parameter $$\alpha$$ and is independent of $$\bs{X}$$. On the other hand, $$\alpha U_\alpha(x, \cdot) = (1 - p) R_p(x, \cdot)$$ where $$p = r \big/ (\alpha + r)$$. We know from our study of discrete potentials that $$(1 - p) R_p(x, \cdot)$$ is the probability density function of $$Y_M$$ where $$M$$ has the geometric distribution on $$\N$$ with parameter $$1 - p$$ and is independent of $$\bs{Y}$$. But also $$X_T = Y_{N_T}$$. So it follows that if $$T$$ has the exponential distribution with parameter $$\alpha$$, $$\bs{N} = \{N_t: t \in [0, \infty)\}$$ is a Poisson process with rate $$r$$, and is independent of $$T$$, then $$N_T$$ has the geometric distribution on $$\N$$ with parameter $$\alpha \big/ (\alpha + r)$$. Of course, we could easily verify this directly, but it's still fun to see such connections.

#### Limiting Behavior and Stationary Distributions

Once again, suppose that $$\bs{X} = \{X_t: t \in [0, \infty)\}$$ is a continuous-time Markov chain on $$S$$ subordinate to the Poisson process with rate $$r \in (0, \infty)$$ and with jump transition matrix $$Q$$. Let $$\bs{Y} = \{Y_n: n \in \N\}$$ denote the jump process. The limiting behavior and stationary distributions of $$\bs{X}$$ are closely related to those of $$\bs{Y}$$.

Suppose that $$\bs{X}$$ (and hence $$\bs{Y}$$) are irreducible and positive recurrent

1. $$g: S \to (0, \infty)$$ is invariant for $$\bs{X}$$ if and only if $$g$$ is invariant for $$\bs{Y}$$.
2. $$f$$ is an invariant probability density function for $$\bs{X}$$ if and only if $$f$$ is an invariant probability density function for $$\bs{Y}$$.
3. $$\bs{X}$$ is null recurrent if and only if $$\bs{Y}$$ is null recurrent, and in this case, $$\lim_{n \to \infty} Q^n(x, y) = \lim_{t \to \infty} P_t(x, y) = 0$$ for $$(x, y) \in S^2$$.
4. $$\bs{X}$$ is positive recurrent if and only if $$\bs{Y}$$ is positive recurrent. If $$\bs{Y}$$ is aperiodic, then $$\lim_{n \to \infty} Q^n(x, y) = \lim_{t \to \infty} P_t(x, y) = f(y)$$ for $$(x, y) \in S^2$$, where $$f$$ is the invariant probability density function.
Proof:

All of these results follow from the basic theory of stationary and limiting distributions for continuous-time chains, and the fact that the exponential parameter function $$\lambda$$ is constant.

#### Time Reversal

Once again, suppose that $$\bs{X} = \{X_t: t \in [0, \infty)\}$$ is a continuous-time Markov chain on $$S$$ subordinate to the Poisson process with rate $$r \in (0, \infty)$$ and with jump transition matrix $$Q$$. Let $$\bs{Y} = \{Y_n: n \in \N\}$$ denote the jump process. We assume that $$\bs X$$ (and hence $$\bs Y$$) are irreducible. The time reversal of $$\bs X$$ is closely related to that of $$\bs Y$$.

Suppose that $$g: S \to (0, \infty)$$ is invariant for $$\bs X$$. The time reversal $$\hat{\bs X}$$ with respect to $$g$$ is also subordinate to the Poisson process with rate $$r$$. The jump chain $$\hat{\bs Y}$$ of $$\hat{\bs X}$$ is the (discrete) time reversal of $$\bs Y$$ with respect to $$g$$.

Proof:

From the previous result, $$g$$ is also invariant for $$\bs Y$$. From the general theory of time reversal, $$\hat{\bs X}$$ has the same exponential parameter function as $$\bs X$$ (namely the constant function $$r$$) and so is also subordinate to the Poisson process with rate $$r$$. Finally, the jump chain $$\hat{\bs Y}$$ of $$\hat{\bs X}$$ is the reversal of $$\bs Y$$ with respect to $$r g$$ and hence also with respect to $$g$$.

In particular, $$\bs X$$ is reversible with respect to $$g$$ if and only if $$\bs Y$$ is reversible with respect to $$g$$. As noted earlier, $$\bs X$$ and $$\bs Y$$ are of the same type: both transient or both null recurrent or both positive recurrent. In the recurrent case, there exists a positive invariant function that is unique up to multiplication by constants. In this case, the reversal of $$\bs X$$ is unique, and is the chain subordinate to the Poisson process with rate $$r$$ whose jump chain is the reversal of $$\bs Y$$.

#### Uniform Chains

In the construction above for a Markov chain $$\bs X = \{X_t: t \in [0, \infty)\}$$ that is subordinate to the Poisson process with rate $$r$$ and jump transition kernel $$Q$$, we assumed of course that $$Q(x, x) = 0$$ for every $$x \in S$$. So there are no absorbing states and the sequence $$(\tau_1, \tau_2, \ldots)$$ of arrival times of the Poisson process are the jump times of the chain $$\bs X$$. However in our introduction to continuous-time chains, we saw that the general construction of a chain starting with the function $$\lambda$$ and the transition matrix $$Q$$ works without this assumption on $$Q$$, although the exponential parameters and transition probabilities change. The same idea works here.

Suppose that $$\bs N = \{N_t: t \in [0, \infty)\}$$ is a counting Poisson process with rate $$r \in (0, \infty)$$ and that $$\bs Y = \{Y_n: n \in \N\}$$ is a discrete-time Markov chain with transition matrix $$Q$$ on $$S \times S$$ satisfying $$Q(x, x) \lt 1$$ for $$x \in S$$. Assume also that $$\bs N$$ and $$\bs Y$$ are independent. Define $$X_t = Y_{N_t}$$ for $$t \in [0, \infty)$$. Then $$\bs X = \{X_t: t \in [0, \infty)\}$$ is a continuous-Markov chain with exponential parameter function $$\lambda(x) = r [1 - Q(x, x)]$$ for $$x \in S$$ and jump transition matrix $$\tilde Q$$ given by $\tilde Q(x, y) = \frac{Q(x, y)}{1 - Q(x, x)}, \quad (x, y) \in S^2, \, x \ne y$

Proof:

This follows from the result in the introduction.

The Markov chain constructed above is no longer a chain subordinate to the Poisson process by our definition above, since the exponential parameter function is not constant, and the transition times of $$\bs X$$ are no longer the arrival times of the Poisson process. Nonetheless, many of the basic results above still apply.

Let $$\bs X = \{X_t: t \in [0, \infty)\}$$ be the Markov chain constructed in the previous theorem. Then

1. For $$t \in [0, \infty)$$, the transition matrix $$P_t$$ is given by $P_t = \sum_{n=0}^\infty e^{- r t} \frac{(r t)^n}{n!} Q^n$
2. For $$\alpha \in (0, \infty)$$, the $$\alpha$$ potential matrix is given by $U_\alpha = \frac{1}{\alpha + r} \sum_{n=0}^\infty \left(\frac{r}{\alpha + r}\right)^n Q^n$
3. The generator matrix is $$G = r (Q - I)$$
4. $$g: S \to (0, \infty)$$ is invariant for $$\bs X$$ if and only if $$g$$ is invariant for $$\bs Y$$.
Proof:

The proofs are just as before.

It's a remarkable fact that every continuous-time Markov chain with bounded exponential parameters can be constructed as in the last theorem, a process known as uniformization. The name comes from the fact that in the construction, the exponential parameters become constant, but at the expense of allowing the embedded discrete-time chain to jump from a state back to that state. To review the definition, suppose that $$\bs X = \{X_t: t \in [0, \infty)\}$$ is a continuous-time Markov chain on $$S$$ with transition semigroup $$\bs P = \{P_t: t \in [0, \infty)\}$$, exponential parameter function $$\lambda$$ and jump transition matrix $$Q$$. Then $$\bs P$$ is uniform if $$P_t(x, x) \to 1$$ as $$t \downarrow 0$$ uniformly in $$x$$, or equivalently if $$\lambda$$ is bounded.

Suppose that $$\lambda: S \to (0, \infty)$$ is bounded and that $$Q$$ is a transition matrix on $$S$$ with $$Q(x, x) = 0$$ for every $$x \in S$$. Let $$r \in (0, \infty)$$ be an upper bound on $$\lambda$$ and $$\bs N = \{N_t: t \in [0, \infty)\}$$ a Poisson counting process with rate $$r$$. Define the transition matrix $$\hat Q$$ on $$S$$ by \begin{align*} \hat Q(x, x) & = 1 - \frac{\lambda(x)}{r} \quad x \in S \\ \hat Q(x, y) & = \frac{\lambda(x)}{r} Q(x, y) \quad (x, y) \in S^2, \, x \ne y \end{align*} and let $$\bs Y = \{Y_n: n \in \N\}$$ be a discrete-time Markov chain with transition matrix $$\hat Q$$, independent of $$\bs N$$. Define $$X_t = Y_{N_t}$$ for $$t \in [0, \infty)$$. Then $$\bs X = \{X_t: t \in [0, \infty)\}$$ is a continuous-time Markov chain with exponential parameter function $$\lambda$$ and jump transition matrix $$Q$$.

Proof:

Note that $$\hat Q(x, y) \ge 0$$ for every $$(x, y) \in S^2$$ and $$\sum_{y \in S} \hat Q(x, y) = 1$$ for every $$x \in S$$. Thus $$\hat Q$$ is a transition matrix on $$S$$. Note also that $$\hat Q(x, x) \lt 1$$ for every $$x \in S$$. By construction, $$\lambda(x) = r[1 - \hat Q(x, x)]$$ for $$x \in S$$ and $Q(x, y) = \frac{\hat Q(x, y)}{1 - \hat Q(x, x)}, \quad (x, y) \in S^2, \, x \ne y$ So the result now follows from the theorem above.

Note in particular that if the state space $$S$$ is finite then of course $$\lambda$$ is bounded so the previous theorem applies. The theorem is useful for simulating a continuous-time Markov chain, since the Poisson process and discrete-time chains are simple to simulate. In addition, we have nice representations for the transition matrices, potential matrices, and the generator matrix.

Suppose that $$\bs X = \{X_t: t \in [0, \infty\}$$ is a continuous-time Markov chain on $$S$$ with bounded exponential parameter function $$\lambda: S \to (0, \infty)$$ and jump transition matrix $$Q$$. Define $$r$$ and $$\hat Q$$ as in the last theorem. Then

1. For $$t \in [0, \infty)$$, the transition matrix $$P_t$$ is given by $P_t = \sum_{n=0}^\infty e^{- r t} \frac{(r t)^n}{n!} \hat Q^n$
2. For $$\alpha \in (0, \infty)$$, the $$\alpha$$ potential matrix is given by $U_\alpha = \frac{1}{\alpha + r} \sum_{n=0}^\infty \left(\frac{r}{\alpha + r}\right)^n \hat Q^n$
3. The generator matrix is $$G = r (\hat Q - I)$$
4. $$g: S \to (0, \infty)$$ is invariant for $$\bs X$$ if and only if $$g$$ is invariant for $$\hat Q$$.
Proof:

These results follow from the theorem above.

### Examples

#### The Two-State Chain

The following exercise applies the uniformization method to the two-state chain.

Consider the continuous-time Markov chain $$\bs X = \{X_t: t \in [0, \infty)\}$$ on $$S = \{0, 1\}$$ with exponential parameter function $$\lambda = (a, b)$$, where $$a, \, b \in (0, \infty)$$. Thus, states 0 and 1 are stable and the jump chain has transition matrix $Q = \left[\begin{matrix} 0 & 1 \\ 1 & 0 \end{matrix} \right]$ Let $$r = a + b$$, an upper bound on $$\lambda$$. Show that

1. $$\hat Q = \frac{1}{a + b} \left[\begin{matrix} b & a \\ b & a \end{matrix} \right]$$
2. $$G = \left[\begin{matrix} -a & a \\ b & - b \end{matrix}\right]$$
3. $$P_t = \hat Q - \frac{1}{a + b} e^{-(a + b) t} G$$ for $$t \in [0, \infty)$$
4. $$U_\alpha = \frac{1}{\alpha} \hat Q - \frac{1}{(\alpha + a + b)(a + b)} G$$ for $$\alpha \in (0, \infty)$$
Proof:

The form of $$\hat Q$$ follows easily from the definition above. Note that the rows of $$\hat Q$$ are the invariant PDF. It then follows that $$\hat Q^n = \hat Q$$ for $$n \in \N_+$$. The results for the transition matrix $$P_t$$ and the potential $$U_\alpha$$ then follow easily from the theorem above.

Although we have obtained all of these results for the two-state chain before, the derivation based on uniformization is the easiest.