2. Makov Process
The future evolution of the process depends only on the instant observation of the system and not on the past.
2.1. A 2-state Markov chain
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Example 1 |
Assume you have 2 shirts, white and blue. Their state space is \(S = {W,B}\). - The probability of you wearing a white shirt and continue wearing the white shirt is 0.7. |
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Solution:
\[\text{2 Shirts} =
\begin{cases}
P(W|W) &= 0.7\\
P(B|W) &= 0.3\\
P(W|B) &= 0.6\\
P(B|B) &= 0.4
\end{cases}\]
(i) At the end of Monday: Mon \(= S \times \text{trans_matrix}\)
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2.2. A 3-state Markov chain
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Example 2 |
There are 3 states: - You only have one bar you go to if you want to go out. |
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Solution:
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