Exercise 1 Let \(X\) be an irreducible aperiodic Markov chain on a finite number of states, started at the state \(s\). Show without using any fancy limit theorems, that \(\mathbb{E} T < \infty\), where
\[T = \inf\{ n \geq 1: X_n =s\}.\]
Exercise 2 Let \(N\) be a Poisson process on \([0, \infty)\). Fix \(T >0\). Show that \(R(t) = N(T-t)\) is a Poisson process on \([0, T]\). Hint: appeal to appropriate construction of Poisson processes.
Exercise 3 By brute force, show that for a \(M(\lambda)/M(\mu)/1\) queue that is started with at stationarity, so that the number of items in the system at time any time \(t\) has distribution
\[\mathbb{P}(Q(t) = n) = \rho^n(1-\rho)\]
has the property that an arrival waits time \(W\) until being served, where \(W\) has law
\[ \mathbb{P}(W \leq x) = 1- \rho e^{-x(\mu - \lambda)}.\]
Exercise 4 We say that a continuous-time Markov chain with generator-(Q matrix) \(G\) is reversible with respect to the probability measure \(\pi\) if
\[ \pi_i g_{ij} = \pi_j g_{ji}\]
for all states \(i,j\).
Show that \(\pi\) must be stationary.
Show the the continuous-time Markov chain corresponding to a \(M(\lambda)/M(\mu)/1\) queue is reversible with respect to its stationary distribution.