Transient Behavior of CTMCS
Since their first specification, Markov models have been widely used for modeling many kinds of systems, in different environments, in application fields, and with different purposes. The success of the formalism of Markov chains is due to the simplicity and the effectiveness in (analytically) evaluating discrete state-space systems, providing both transient and steady-state analyses. But the Markovian assumption often limits the applicability of Markov chains. This limitation can be overcome by nonhomogeneous Markov chains and phase type distributions, allowing us to model more general behaviors. This further enhances the importance and the effectiveness of Markov chains and related formalisms. In this article, we focus on the transient behavior of Markov models, providing an overview of their solution techniques.
