Finding ##P(X_2 = 2)## of a Markov Chain

In summary, the solutions provided are correct. However, for pedantic reasons, the notation for the expectation of the variable should be written as ##E[X_2]## instead of ##E[X_2 = 2]##. This is important to note in case the teacher is also pedantic and may deduct points for incorrect notation.
  • #1
user366312
Gold Member
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Homework Statement
If ##(X_n)_{n≥0}## is a Markov chain on ##S = \{1, 2, 3\}## with initial distribution ##α = (1/2, 1/2, 0)## and transition matrix

## \begin{bmatrix} 1/2&0&1/2\\ 0&1/2&1/2\\ 1/2&1/2&0 \end{bmatrix},##

then ##P(X_2 = 2) = ?## and ##E(X_2)=?##.
Relevant Equations
Markov Chain
My solution:

##X_1 = \begin{bmatrix} 1/2&1/2&0 \end{bmatrix} \begin{bmatrix} 1/2&0&1/2\\ 0&1/2&1/2\\ 1/2&1/2&0 \end{bmatrix} = \begin{bmatrix} 1/4&1/4&1/2 \end{bmatrix}##

##X_2 = \begin{bmatrix} 1/4&1/4&1/2 \end{bmatrix} \begin{bmatrix} 1/2&0&1/2\\ 0&1/2&1/2\\ 1/2&1/2&0 \end{bmatrix} = \begin{bmatrix} 3/8&3/8&1/4 \end{bmatrix}##

So, ##P(X_2=2) = 3/8##

##E(X_2=2) = 1 * 3/8 + 2 * 3/8 + 3 * 1/4 = 15/8##
___

Is this solution correct?

Why or why not?
 
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  • #2
You have done all the work correctly, so your solutions are correct.
 
  • #3
user366312 said:
Is this solution correct?

Not quite for pedantic reasons.

user366312 said:
Why or why not?

Because you wrote ##E[X_2 = 2]## and what you're calculating is the expectation of the variable ##X_2##, so the proper notation is ##E[X_2]##.

Pedantic, as I admit. But if your teacher is pedantic as well, you don't want to lose points for silly notational stuff like that.
 

1. What is a Markov Chain?

A Markov Chain is a mathematical model used to describe a sequence of events where the probability of each event depends only on the previous event. It is a type of stochastic process that follows the Markov property, which states that the future state of the system depends only on the present state and not on the sequence of events that preceded it.

2. How do you calculate the probability of a specific state in a Markov Chain?

The probability of a specific state in a Markov Chain can be calculated by finding the steady-state vector of the transition matrix. This can be done by solving a system of linear equations or by using iterative methods such as the power method or the Markov Chain Monte Carlo method.

3. What is the significance of finding ##P(X_2 = 2)## in a Markov Chain?

Finding ##P(X_2 = 2)## in a Markov Chain is significant because it represents the probability of the system being in state 2 at time step 2, given the initial state and the transition probabilities. This information can be used to make predictions about the future behavior of the system.

4. Can the probability of a specific state in a Markov Chain change over time?

Yes, the probability of a specific state in a Markov Chain can change over time as the system evolves. This is because the transition probabilities between states can change, leading to different probabilities for each state at different time steps.

5. How is the concept of "absorbing states" related to finding ##P(X_2 = 2)## in a Markov Chain?

Absorbing states in a Markov Chain are states that, once reached, the system remains in forever. Finding ##P(X_2 = 2)## in a Markov Chain is related to absorbing states because it can help determine the likelihood of the system reaching an absorbing state at a specific time step, which can be useful in analyzing the long-term behavior of the system.

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