Often in this course we started an example with “Let \(X_1, .., X_n\) be an iid sequence …”. This independence assumption makes a lot of sense in many statistics problems, mainly when the data comes from a random sample. But what happens if the data is not independent?

In that case the most important thing is to describe the dependence structure of that data, that is to say what the relationship of \(X_i\) and \(X_k\) is. There many such structures. In this section we will study one of the most common, called a Markov chain.

The sequence of r.v. \(X_1\), \(X_2\), .. is said to be a Markov chain if for any event \(A\) we have \[ P(X_n \in A | X_1 = x_1, .., X_{n-1}=x_{n-1}) = P(X_n \in A | X_{n-1} = x_{n-1}) \] that is \(X_n\) depends only on \(X_{n-1}\) but not on any of the r.v. before it.

**Example** (Random Walk) Say we flip a coin repeatedly. Let the random variable \(Y_i\) be 1 if the \(i^th\) flip is heads, -1 otherwise.

Now let \[ X_n = \sum^n_{i=1} Y_i \] Clearly we have

If we think of the index n as a time variable, then all that matters for the state of the system at time n is where it was at time n-1, but not on how it got to that state.

The random walk above is a discrete-time stochastic process because the time variable n takes discrete values (1,2, etc.) There are also continuous time stochastic processes {X(t),t≥0}.

Our random walk example is also a discrete state space stochastic process because the r.v. X_{n} can only take countably many values (0, ±1,±2 etc.). Other stochastic processes might have a continuous state space.

For a Markov chain (that is a stochastic process with a discrete state space) all the relevant (probability) information is contained in the probability to get from state i to state j in k steps. For k=1 this is contained in the transition matrix P = (p_{ij}), and in fact as we shall see that P is all we need.

**Example** (Random Walk, cont)

Here we have p_{ij} = 1/2 if |i-j|=1, 0 otherwise

**Example** (Asymmetric Random Walk)

As above the state space are the integers but now we go from i to i+1 with probability p, to i-1 with probability q and stay at i with probability 1-p-q.

**Example** (Ehrenfest chain)

Say we have two boxes, box 1 with k balls and box 2 with r-k balls. We pick one of the balls at random and move it to the other box. Let X_{n} be the number of balls in box 1 at time n. We have \[
\begin{aligned}
&X_n \in {0,1,..,r} \\
&p_{k,k+1} = (r-k)/r \\
&p_{k,k-1} = k/r \\
&p_{i,j} = 0 \text{ otherwise}
\end{aligned}
\] Ehrenfest used this model to study the exchange of air molecules in two chambers connected by a small hole, so small that only one molecule at a time can pass through.

Say we have a Markov chain \(X_n\), \(n=1,2,..\) with transition matrix P. Define the n-step transition matrix \(P^{(n)}\) = \((p^{(n)}_{ij})\) by \[ p^{(n)}_{ij} = P(X_n=j|X_1=i) \] Of course \(P^{(1)} = P\). Now

**Example** (Ehrenfest chain)

Let’s find the 2-step transition matrix for the Ehrenfest chain with \(r=3\). The transition matrix is given by

and so the 2-step transition matrix is

In order to find P^{(n)} we could just find PPP..P n-times. With a little linear algebra this becomes easier: For many matrices P there exists a matrix U and a diagonal matrix D such that \[
P=UDU^{-1}
\] Here is how to find U and D:

First we need to find the eigenvalues of the matrix P, that is we need to find the solutions of the equations \[ Px=\lambda x \] This is equivalent to \[ \begin{aligned} &(P-\lambda I)x=0 \\ &\text{or} \\ &det(P-\lambda I)=0 \end{aligned} \] So we have:

The D above now is the matrix with the eigenvalues on the diagonal.

The columns of the matrix U are the corresponding eigenvectors (with Euclidean length 1), so for example

Of course we have det(P-λI)=0, so this system is does not have a unique solution. Setting \(x_1=1\) we can then easily find a solution x=(1,-1,1,-1).

This vector has Euclidean length √(1^{2}+(-1)^{2}+1^{2}+(-1)^{2}) = 2, so the normalized eigenvector is x=(1/2,-1/2,1/2,-1/2)

Similarly we can find eigenvectors for the other eigenvalues.

Alternatively (and a lot easier!) we can use the R function **eigen** to do the calculations for us!

Why does this help in computing \(P^{(n)}\)? It turns out that we have \[ P^{(2)} = PP = UDU^{-1}UDU = UDDU^{-1} = UD^2 U^{-1} \] and

and in general we have \(P^{(n)} = UD^n U^{-1}\).

Let’s find the 2-step and 3-step transition matrix of the Ehrenfest chain with \(r=3\):

```
r <- 3
statespace <- 0:r
pi <- choose(rep(r, r + 1), 0:r)/2^r
P <- matrix(0, r + 1, r + 1)
dimnames(P) <- list(statespace, statespace)
P[1, 2] <- 1
P[r + 1, r] <- 1
for (k in 2:r) {
P[k, k - 1] <- (k - 1)/r
P[k, k + 1] <- (r - k + 1)/r
}
print(P, 3)
```

```
## 0 1 2 3
## 0 0.000 1.000 0.000 0.000
## 1 0.333 0.000 0.667 0.000
## 2 0.000 0.667 0.000 0.333
## 3 0.000 0.000 1.000 0.000
```

```
D <- diag(eigen(P)$values)
U <- eigen(P)$vectors
print(round(U %*% D^2 %*% solve(U), 3))
```

```
## [,1] [,2] [,3] [,4]
## [1,] 0.333 0.000 0.667 0.000
## [2,] 0.000 0.778 0.000 0.222
## [3,] 0.222 0.000 0.778 0.000
## [4,] 0.000 0.667 0.000 0.333
```

`print(round(U %*% D^3 %*% solve(U), 3))`

```
## [,1] [,2] [,3] [,4]
## [1,] 0.000 0.778 0.000 0.222
## [2,] 0.259 0.000 0.741 0.000
## [3,] 0.000 0.741 0.000 0.259
## [4,] 0.222 0.000 0.778 0.000
```

The routine **ehrenfest** with which=1 computes $P^{(n)} for the Ehrenfest chain.

Consider again the Ehrenfest chain, and compute \(P^{(n)}\) for n→∞:

`print(round(U %*% D^10 %*% solve(U), 3))`

```
## [,1] [,2] [,3] [,4]
## [1,] 0.25 0.00 0.75 0.00
## [2,] 0.00 0.75 0.00 0.25
## [3,] 0.25 0.00 0.75 0.00
## [4,] 0.00 0.75 0.00 0.25
```

`print(round(U %*% D^100 %*% solve(U), 3))`

```
## [,1] [,2] [,3] [,4]
## [1,] 0.25 0.00 0.75 0.00
## [2,] 0.00 0.75 0.00 0.25
## [3,] 0.25 0.00 0.75 0.00
## [4,] 0.00 0.75 0.00 0.25
```

You notice that \(P^{(n)}\) seems to converge to a limit. We will now study this limit.

Let S be the state space of a Markov Chain X with transition matrix P. Let π be a “measure” on S. Then π is called a stationary measure of X if \(\pi^T P=\pi^T\).

We won’t discuss exactly what it means for π to be a “measure”. You can think of it in the same way as a probability distribution, only that we don’t have \(\sum \pi_i=1\).

Note: \[ \pi^TP=\pi^T \text{ iff } (P^T\pi)^T=\pi^T \text{ iff } P^T\pi=\pi \text{ iff } (P^T-I)\pi=0 \] so again the system of equations is singular.

**Example** (Ehrenfest chain) To find a (?) stationary measure we have to solve the system of equations \[
p_{ij} = \sum_i \pi_i P_{ij} \text{ }i=0,1..,r
\] often we can get unique solution by requiring that π be a proper probability distribution, that is that ∑π_{i} = 1.

which has the solution \[ \pi = (1/8,3/8,3/8,1/8) \] In the general case we find the stationary measure \[ \pi_i=\begin{pmatrix} r \\ j \end{pmatrix}/2^j \] \(i=0,..,r\).

The interpretation is the following: Say we choose the initial state \(X_0\) according to \(\pi\), that is \(P(X_0=i) = \pi_i\). Then \(\pi_i\) is the long-run proportion of time the chain spends at state i, that is

\[ \pi_i = \lim_{N\rightarrow \infty} \sum^N_{k=1} I[X_n=i]/N \]

**Example** For the Ehrenfest chain Let’s illustrate this for the Ehrenfest chain with r=3:

```
r <- 3
statespace <- 0:r
pi <- choose(rep(r, r + 1), 0:r)/2^r
n <- 1000
X <- rep(-1, n)
X[1] <- sample(statespace, size = 1, prob = pi)
for (i in 2:n) {
if (X[i - 1] == 0) {
X[i] <- 1
next
}
if (X[i - 1] == r) {
X[i] <- r - 1
next
}
X[i] <- sample(c(X[i - 1] + 1, X[i - 1] - 1), size = 1, prob = c((r - X[i - 1])/r, X[i - 1]/r))
}
print(rbind(pi, sim=table(X)/n), 3)
```

```
## 0 1 2 3
## pi 0.125 0.375 0.375 0.125
## sim 0.125 0.367 0.375 0.133
```

**Example** (Random Walk) Let S be the integers and define a Markov chain by p_{i,i+1} = p and p_{i,i-1} = q = 1-p. A stationary measure is given by π_{i}=1 for all i because (πP)_{i} = 1p+1q = 1.

Now assume p≠q and define π_{i} =(p/q)^{i}. Then

Note that this shows that stationary measure are not unique.

One use of the stationary distribution is an extension of the WLLN to Markov chains. That is, say h is a function on the state space, then

where Z is a r.v. with density π.

This is illustrated in **ehrenfest** with which=3 for h(x)=x, h(x)=x^{2} and h(x)=log(x+1)

One of the main results for Markov chains is the following:

If the Markov chain {X_{n}} is irreducible and ergodic, then

**Example:** Of course this result does not apply to the Ehrenfest chain, which is not aperiodic, but the result holds anyway as we have seen.

**Example:** consider the following Markov chain: if at i it next moves to i+1 with probability p or to 1 with probability 1-p. Let’s find its stationay distribution:

so the stationary distribution is a geometric!

Let’s do a simulation and check, done in **mark1()**