Write the stationary probabilities as . The equation reduces to . Together with , this gives
The positive transition probabilities make the two states communicate, so the stationary distribution is unique.
The Markov property and conditioning on the intermediate state give, for the full history through time ,
Condition this identity again on the smaller history . Its right-hand side is already determined by , so the tower property gives
Thus the sampled sequence is a time-homogeneous Markov chain with transition matrix . Checking only its one-time marginal distributions would not by itself establish the Markov property.
It is the every-second-step case of a subsampled Markov chain.
The chain alternates at every step between the two classes and . It is irreducible and has period two: returns require an even number of steps, and a two-step return has positive probability.
Writing the off-diagonal block as , its square is . Hence
Every two steps remain within the initial class, where the sampled chain is irreducible and aperiodic.
State can be reached from state only at an even time. In the two-step sampled chain, each step while still at state reaches state with probability , and otherwise stays at state with probability . Thus, if is the original hitting time,
Equivalently where has the geometric distribution on positive integers with parameter .
On the class , the two-step transition matrix is symmetric with positive entries, so its stationary distribution is and its distribution converges to that law. Therefore
Explicitly the two eigenvalues are and , giving . The original chain has probability zero of being in state at odd times, consistent with its period two.

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