First realize the chain as a random walk on a multigraph: put three parallel edges between , two between , and one between . There are six edges and vertex degrees . Choosing an incident edge uniformly gives exactly the stated transition probabilities. The stationary distribution of a graph random walk is the degree divided by twelve, , and detailed balance follows because each edge multiplicity contributes the same flux in both directions.
For the first positive return time, Kac's lemma gives
For passage to , put and . The first-step equations are and , since reaching stops the clock. Thus
A multigraph is essential: a simple graph on three vertices cannot have six edges.
Let be the expected time to complete the specified directed transition, starting at . This is a transition-completion time in a Markov chain, rather than merely a first visit to . First-step decomposition gives
The missing continuation in the middle equation represents successful completion, but its step is counted by the leading one. Substitution gives
Thus the requested expectation from is steps.
The chain is a reversible Markov chain, so detailed balance makes the stationary path probability
A path contains an ordered occurrence of followed later by exactly when its reversal contains followed later by . These occurrences need not be adjacent. Therefore for every , and in particular the two finite expectations are equal. This is ordered-state visit times in a reversible chain.
To decompose the expectations, distinguish the entrance time , which allows time zero and has , from the positive return convention in part (i). The Strong Markov property at the first visit to or gives
Equating them yields the difference formula with . Replacing by in the sum adds , again by Kac's lemma. Hence
For a numerical consistency check, the other entrance times are and , while . Both sides of the displayed identity equal .

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