STEADY-STATE SOLUTIONS OF MARKOV CHAINS

The paper is devoted on methods and algorithms for steady-state analysis of Markov chains. Basic, direct and iterative methods for steady-state analysis of Markov chains are concerned, where Gaussian Elimination method and Grassman method, as well as Power, Jacobi’s and Gauss-Seidel’s methods are implemented. Algorithms for computation of steady-state probability vector for finite Markov chains are developed. Comparison of numerical solutions to exact equilibrium solution for local-balance equation of Discrete-Time Markov Chain is given. Example and numerical results for feedback networks of Markovian queues are shown.