APPLICATION OF POWER NUMERICAL METHOD FOR THE STATIONARY DISTRIBUTION OF MARKOV CHAIN
The evolution of this model is represented by transitions from one state to the next. Also, the physical or mathematical behavior of this system can also be illustrated by identifying all of the possible states and explaining how it transitions between them. The iterative solution approaches for the stationary distribution of Markov chains, which begin with an initial estimate of the solution vector and it becomes closer and closer to the true solution with each iteration are investigated. Our goal is to compute solutions of stationary distribution of Markov chain by utilizing the power iterative method which leaves the transition matrices unchanged and saves time by considering the discretization effect, and the convergency. Matrices operations such as multiplication with one or more vectors, lower, diagonal and upper concepts of matrix, with the help of several existing Markov chain laws, theorems, formulas, and the normalization principle are applied. For the illustrative examples, the stationary distribution vectors and table of convergence are obtained.
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