Application of Discrete-Time Markov Models
This paper introduced a general class of mathematical models, Markov chain models, which are appropriate for modeling of phenomena in the physical life, medicine, engineering and social sciences. Application of Markov chains are quite common and have become a standard tool of decision making. What matters in predicting the future of the system is its present state, and not the path by which the system got to its present state. Two methods are presented that exemplify the flexibility of this approach: the regular Markov chain and absorbing Markov chain. The long-term trend in absorbing Markov chains depends on the initial state. In addition, changing the initial state can change the final result. This property distinguishes absorbing Markov chains from regular Markov chains, where the final result is independent of the initial state. The problems are formulated by using the Wolfram Mathematical Programming System.