Let . The residue contains all information needed for the next residue, so is a four-state Markov chain. In the order , its transition matrix is
For example, from residue , a head returns to and a tail goes to ; from either odd residue the tail leaves that residue unchanged.
The stationary distribution solves . Its equations give , , and . Normalization therefore gives
The Markov chain is irreducible: the positive-probability transitions around reach all states. It is also aperiodic because it has self-loops. The finite irreducible Markov chain ergodic theorem says that empirical state frequencies converge almost surely to the unique stationary distribution, irrespective of the initial state. Hence
Aperiodicity also permits convergence of the one-time state probabilities, although it is not needed for this empirical-frequency theorem.

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