A First Course in Probability (10th Edition)
A First Course in Probability (10th Edition)
10th Edition
ISBN: 9780134753119
Author: Sheldon Ross
Publisher: PEARSON
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**Markov Chain Example**

Given the Markov Chain \((S, x_0, P)\) where \(S = \{s_1, s_2, s_3\}\), \(x_0 = \begin{bmatrix} 0 \\ 0 \\ 1 \end{bmatrix}\), and

\[ P = \begin{bmatrix}
0 & .3 & .2 \\
1 & 0 & .4 \\
0 & .7 & .4 
\end{bmatrix} \]

\[P^2 = \begin{bmatrix}
.3 & .14 & .2 \\
0 & .58 & .36 \\
.7 & .28 & .44 
\end{bmatrix} \]

\[P^3 = \begin{bmatrix}
.14 & .23 & .196 \\
.58 & .252 & .376 \\
.28 & .518 & .428 
\end{bmatrix} \]

\[P^4 = \begin{bmatrix}
.23 & .1792 & .1984 \\
.252 & .4372 & .3692 \\
.518 & .3836 & .4344 
\end{bmatrix} \]

\[P^5 = \begin{bmatrix}
.1792 & .20788 & .19704 \\
.4372 & .33264 & .37218 \\
.3836 & .45948 & .4308 
\end{bmatrix} \]

\[P^6 = \begin{bmatrix}
.20788 & .191688 & .197808 \\
.33264 & .391672 & .36936 \\
.45948 & .41664 & .432832 
\end{bmatrix} \]

Compute the probability that the Markov Chain is in state \(s_1\) at time 6.

---

**Interpretation of Matrices:**

In this case, each matrix \(P^n\) represents the state transition probabilities after \(n\) steps.

For example:

- \(P^2\) indicates the state probabilities after 2 steps.
- \(P^3\) indicates the state probabilities after 3 steps.
- and so on...

As \(n\) increases, \(P^n\) gives the probabilities of being in each state after \(n\) transitions, starting
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Transcribed Image Text:**Markov Chain Example** Given the Markov Chain \((S, x_0, P)\) where \(S = \{s_1, s_2, s_3\}\), \(x_0 = \begin{bmatrix} 0 \\ 0 \\ 1 \end{bmatrix}\), and \[ P = \begin{bmatrix} 0 & .3 & .2 \\ 1 & 0 & .4 \\ 0 & .7 & .4 \end{bmatrix} \] \[P^2 = \begin{bmatrix} .3 & .14 & .2 \\ 0 & .58 & .36 \\ .7 & .28 & .44 \end{bmatrix} \] \[P^3 = \begin{bmatrix} .14 & .23 & .196 \\ .58 & .252 & .376 \\ .28 & .518 & .428 \end{bmatrix} \] \[P^4 = \begin{bmatrix} .23 & .1792 & .1984 \\ .252 & .4372 & .3692 \\ .518 & .3836 & .4344 \end{bmatrix} \] \[P^5 = \begin{bmatrix} .1792 & .20788 & .19704 \\ .4372 & .33264 & .37218 \\ .3836 & .45948 & .4308 \end{bmatrix} \] \[P^6 = \begin{bmatrix} .20788 & .191688 & .197808 \\ .33264 & .391672 & .36936 \\ .45948 & .41664 & .432832 \end{bmatrix} \] Compute the probability that the Markov Chain is in state \(s_1\) at time 6. --- **Interpretation of Matrices:** In this case, each matrix \(P^n\) represents the state transition probabilities after \(n\) steps. For example: - \(P^2\) indicates the state probabilities after 2 steps. - \(P^3\) indicates the state probabilities after 3 steps. - and so on... As \(n\) increases, \(P^n\) gives the probabilities of being in each state after \(n\) transitions, starting
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