How long until the chain first reaches a target state? First-passage times solve a linear system, just like absorption times. And Kac's lemma ties it back to the stationary distribution with startling economy: the mean time to return to a state i is exactly 1/πᵢ. The rarer the state — the smaller its long-run share — the longer the wait; a state visited a sixth of the time is returned to every six steps.
Compute first-passage times by relaxation, then check Kac's lemma directly: the mean return time to each state equals one over its stationary probability, so the rare state waits proportionally longer: live demo
“A frequently-visited state and a rare one take similar times to return to.” — no; the relationship is exact and inverse. Kac's lemma: mean return time = 1/(stationary probability). Halve a state's long-run share and you exactly double its expected return time. cited
Kac closes the loop with the stationary distribution: π is not just where the chain lives long-run — its reciprocal is literally how often, in steps, each state comes back around. Kac 1947
On the canonical compiler, first-passage times for the weather chain, fed into the return-time formula, land exactly on 1/π — the common state returns in 1.2 steps, the rare one in 6: