Abstract
Markov chain is a series of events in which the conditional probability upcoming events only depend on the current events and do not depend on the previous occurrence. Transition probabilities at the steady state level (steady state probability) is a transition probability that has reached equilibrium, so that it will not change with time or phase changes that occur. This paper determines the eigenvalue states and diagonalization to determine the steady state. The solution is obtained by peeling definitions and theorems. Key words: matrix, matrix diagonalization, Markov chain
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