A * Markov chain,* named after

*Andrey Markov*is a mathematical system that experiences transitions from one state to another, between a finite or countable number of possible states.

**Markov chain**is a random process usually characterized as memoryless: the next state depends only on the current state and not on the sequence of events that preceded it. This specific kind of memorylessness is called the

*.*

**Markov property***have many applications as statistical models of real-world processes.*

**Markov chains**If the random variables $X_{n-1}$ and $X_n$ take the values $X_{n-1}=i$ and $X_n=j$, then the system has made a transition $S_i \rightarrow S_j$, that is, a transition from state $S_i$ to state $S_j$ at the $n$th trial. Note that $i$ can equal $j$, so transitions within the same state may be possible. We need to assign probabilities to the transitions $S_i \rightarrow S_j$. Generally in the chain, the probability that $X_n=j$ will depend on the whole sequence of random variables starting with the initial value $X_0$.

The Markov chain has the characteristic property that the probability that $X_n=j$ depends only on the immediately previous state of the system. This means that we need no further information at each step other than for each $i$ and $j$, \[P\{X_n=j|X_{n-1}=i\}\]

which means the probability that $X_n=j$ given that $X_{n-1}=i$: this probability is independent of the values of $X_{n-2},X_{n-3},\cdots, X_0$.

Let us have a set of states $S=\{s_1,s_2,\cdots,s_n\}$. The process starts in one of these states and moves successively from one state to another. Each move is called a step. If the chain is currently in state $s_i$ then it moves to state $s_j$ at the next step with a probability denoted by $p_{ij}$ (transition probability) and this probability does not depend upon which states the chain was in before the current state. The probabilities $p_{ij}$ are called transition probabilities ($s_i \xrightarrow[]{p_{ij}} s_j$ ). The process can remain in its state, and this occurs in probability $p_{ii}$.

An initial probability distribution, defined on $S$ specifies the starting state. Usually, this is done by specifying a particular state as the starting state.

A **Markov chain** is a sequence of random variables $X_1, X_2,\cdots,$ with the Markov property that, given the present state, the future and past state are independent. Thus

\[P(X_n=x|X_1=x_1,X_2=x_2\cdots X_{n-1}=x_{n-1})\]

\[\quad=P(X_n=x|X_{n-1}=x_{n-1})\]

Or

\[P(X_n=j|X_{n-1}=i)\]

**Example: Markov Chain**

A **Markov chain** $X$ on $S=\{0,1\}$ is determined by the initial distribution given by $p_0=P(X_0=0), \; p_1=P(X_0=1)$ and the one-step transition probability given by $p_{00}=P(x_{n+1}=0|X_n=0)$, $p_{10}=P(x_{n+1}=0|X_n=1)$, $p_{01}=1-p_{00}$ and $p_{11}=1-p_{10}$, so one-step transition probability in matrix form is $P=\begin{pmatrix}p_{00}&p_{10}\\p_{01}&p_{11}\end{pmatrix}$

**References:**

- https://en.wikipedia.org/wiki/Markov_chain
- http://people.virginia.edu/~rlc9s/sys6005/SYS_6005_Intro_to_MC.pdf
- http://www.dartmouth.edu/~chance/teaching_aids/books_articles/probability_book/Chapter11.pdf