This section is intended as a brief introductory recap of Markov chains. A much fuller explanation and introduction is provided in standard texts e.g. Norris, Bremaud, or Levin & Peres (see references below).

## Introductory example: snakes and ladders

We highlight some of the key properties of Markov chains: how to calculate transitions, how the past effects the current movement of the processes, how to construct a chain, what the long run behavior of the process may (or may not) look like. We give an initial example to better position our intuition.

Below in Figure [Snakes_and_ladders], we are given a game of snakes and ladders (or shoots and ladders in the US). Here a counter (coloured red) is placed on the board at the start. You roll a dice. You move along the numbered squares by an amount given by the dice. The objective is to get to the finish. If the counter lands on a square with a snake’s head, you must go back to the square at the snakes tail and, if you land on a square at the bottom of a ladder, go to the top of the ladder.

We let be the position of the counter on the board after the dice has been thrown times. The processes is a discrete time Markov chain. Two things to note: First, note that given the counter is currently at a state, e.g. on square , the next square reached by the counter – or indeed the sequence of states visited by the counter after being on square – is not effected by the path that was used to reach the square. I.e. This is called the Markov Property. Second, notice each movement of the counter from one state is a function of two pieces of information the current state and the independent random roll of the dice. In this way we can construct (or simulate) the random process.

## Definitions

Let be a countable set. An initial distribution

is a positive vector whose components sums to one. A transition matrix is a postive matrix whose rows sum to one, that is, for

With an initial distribution and a transition matrix , you can define a Markov chain. Basically determines the probability the process starts in state Vand gives the probability of going to if you are currently in state .

**Def** [Discrete Time Markov Chain] We say that a sequence of random variables is a discrete time Markov chain, with initial distribution and transition matrix if for ,

and

The condition is often called the Markov property and is the key defining feature of a Markov chain or, more generally, Markov process. It states that the past and future are conditionally independent of the present . Otherwise stated, is says that, when we know the past and present states , the distribution of the future states is only determined by the present state . Think of a board game like snakes and ladders, where you go in the future is only determined by where you are now and not how you got there; this is the Markov property.

The following proposition shows that the evolution of a Markov chain can be constructed from its the current state and an independent dice throw.

**Prop 1** [Constructing Markov Chains] Take a function , a random variable on , and , independent uniform random variables. The sequence constructed with the recursion

is a discrete time Markov chain. Moreover all discrete time Markov chains can be constructed in this way.

The following proposition will be useful when we want to sum up a long sequence of rewards.

**Prop 2** [Markov Chains and Potential Functions] For be a bounded function and for ,

is the unique solution to the equation

Moreover, if function satisfies

then , .

The following is an alternative formulation of the previous proposition.

**Prop 3.** Let be a subset of and let be the hitting time on i.e. and take then

There is a close connection between Markov chains and Martingales that we will want to use later when considering Markov Decision Processes.

**Prop 4** [Markov Chains and Martingale Problems] Show that a sequence of random variables is a Markov chain if and only if, for all bounded functions , the process

is a Martingale with respect to the natural filtration of . Here for any matrix, say , we define

**Some references**

- Norris, J.R., 1997. Markov chains. Cambridge University Press.
- Bremaud, P., 2013. Markov chains: Gibbs fields, Monte Carlo simulation, and queues (Vol. 31). Springer Science & Business Media.
- Levin, D.A. and Peres, Y., 2017. Markov chains and mixing times (Vol. 107). American Mathematical Soc..

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