MathAlgebraLinear algebraMatrix decomposition

Diagonalization of matrices

Predicting the weather

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We live in a city with only 3 weather conditions: Foggy, Hot, and Stormy. Ordering the states as mentioned, the transition probabilities are: A=[0.60.20.20.20.60.20.20.20.6]A=\begin{bmatrix} 0.6 & 0.2 & 0.2 \\ 0.2 & 0.6 & 0.2 \\ 0.2 & 0.2 & 0.6 \\ \end{bmatrix}
Matrix AA is diagonalizable with matrices P=[111110101]P = \begin{bmatrix} 1 & -1 & -1 \\ 1 & 1 & 0 \\ 1 & 0 & 1 \end{bmatrix}, D=[10002500025]D = \begin{bmatrix} 1 & 0 & 0 \\ 0 & \frac{2}{5} & 0 \\ 0 & 0 & \frac{2}{5} \\ \end{bmatrix}and P1=[131313132313131323]P^{-1} = \begin{bmatrix} \frac{1}{3} & \frac{1}{3} & \frac{1}{3} \\ -\frac{1}{3} & \frac{2}{3} & -\frac{1}{3} \\ -\frac{1}{3} & -\frac{1}{3} & \frac{2}{3} \end{bmatrix}.

If it is hot today, what is the probability that the day after tomorrow there will be fog? Give the result to 22 decimal places.

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