## 11.2.1 Introduction

Consider a discrete-time random process $\big\{X_m, m=0,1,2,\dots\}$. In the very simple case where the $X_m$'s are independent, the analysis of this process is relatively straightforward. In this case, there is no "memory" in the system, so each $X_m$ can be looked at independently from previous ones.

However, for many real-life processes, the independence assumption is not valid. For example, if $X_m$ is the stock price of a company at time $m \in \{0,1,2,..\}$, then it is reasonable to assume that the $X_m$'s are dependent. Therefore, we need to develop models where the value of $X_m$ depends on the previous values. In a Markov chain, $X_{m+1}$ depends on $X_{m}$, but given $X_{m}$, it does not depend on the other previous values $X_0$, $X_1$, $\cdots$, $X_{m-1}$. That is, conditioned on $X_m$, the random variable $X_{m+1}$ is independent of the random variables $X_0$, $X_1$, $\cdots$, $X_{m-1}$.

Markov chains are usually used to model the evolution of "states" in probabilistic systems. More specifically, consider a system with the set of possible states $S=\{s_1, s_2,...\}$. Without loss of generality, the states are usually chosen to be $0$, $1$, $2$, $\cdots$, or $1$, $2$, $3$, $\cdots$, depending on which one is more convenient for a particular problem. If $X_n=i$, we say that the system is in state $i$ at time $n$. The idea behind Markov chains is usually summarized as follows: "conditioned on the current state, the past and the future states are independent."

For example, suppose that we are modeling a queue at a bank. The number of people in the queue is a non-negative integer. Here, the state of the system can be defined as the number of people in the queue. More specifically, if $X_n$ shows the number of people in the queue at time $n$, then $X_n \in S=\{0,1,2,...\}$. The set $S$ is called the *state space* of the Markov chain. Let us now provide a formal definition for discrete-time Markov chains.

__Discrete-Time Markov Chains__

Consider the random process $\{X_n, n=0,1,2,\cdots\}$, where $R_{X_i}=S \subset \{0,1,2,\cdots\}$. We say that this process is a

**Markov chain**if \begin{align*} &P(X_{m+1}=j |X_{m}=i, X_{m-1}=i_{m-1}, \cdots, X_{0}=i_{0})\\ &\hspace{80pt}= P(X_{m+1}=j |X_{m}=i), \end{align*} for all $m, j, i, i_0, i_1, \cdots i_{m-1}$. $\\ \\$ If the number of states is finite, e.g., $S=\{0,1,2,\cdots, r\}$, we call it a

**finite**Markov chain.

**transition probabilities**. We assume that the transition probabilities do not depend on time. That is, $P(X_{m+1}=j |X_{m}=i)$ does not depend on $m$. Thus, we can define