Past exam of the probability course of the University of Oxford / 2017 / 1 / c / Solution

The update defines a time-homogeneous Markov process: given its present value, the next value depends only on independent fresh input. Part (b)(i) identifies an invariant law: initializing with the rate-one exponential distribution preserves that distribution at every time. Part (b)(iii) shows convergence of the one-time laws to this invariant law from every deterministic initial value.

For a countable state space, the analogous countable-state Markov chain convergence theorem says that an irreducible Markov chain that is aperiodic and positive recurrent converges to its unique stationary distribution from every initial state. Here the state space is continuous, so that countable-state theorem does not directly supply the proof; the explicit coupling does. The two conclusions have the same equilibrium interpretation.