Periodic strong ergodicity in non-homogeneous Markov systems
Copyright policy )Periodic strong ergodicity in non-homogeneous Markov systems
This paper presents a unified treatment of the convergence properties of nonhomogeneous Markov systems under different sets of assumptions. First the periodic case is studied and the limiting evolution of the individual cyclically moving subclasses of the state space of the associated Markov replacement chain is completely determined. A special case of the above result is the aperiodic or strongly ergodic convergence. Two numerical examples from the literature on manpower planning highlight the practical aspect of the theoretical results.
Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.), Markov replacement chain, stochastic population models, strongly ergodic convergence, manpower planning
Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.), Markov replacement chain, stochastic population models, strongly ergodic convergence, manpower planning
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