Depending on the state space and parameter space, Markov processes can be categorised into several groups. A Markov chain is a Markov process with discrete state space. A Markov chain with discrete parameter space is known as a DTMC, which was explored in the previous two chapters. After learning about the DTMC, one would be interested in studying a continuous-time Markov chainMarkov chainscontinuous-time in which transitions can occur at any time. It is a very useful class of stochastic processes and is abbreviated as CTMC. For example, the number of customers in a coffee shop and the number of planes at airport gates are both instances of continuous-time Markov chains (CTMC). The limiting and stationary distributions of the CTMCs are discussed in this chapter. Poisson process is an example of a discrete state continuous-time Markov process and will be studied in detail in Chap. 5 .