A chain is reversible when, in equilibrium, the probability flux from i to j equals the flux back: πᵢ Pᵢ⫺ = π⫺ P⫺ᵢ for every pair. This detailed balance is stronger than mere stationarity (which only balances total in-flow against out-flow at each state), and it is the trick that lets you design a chain with any target π you want — the foundation under Markov-chain Monte Carlo.
Build a Metropolis chain for a chosen target π, then read the flux table — each ordered pair i→j carries exactly as much probability as j→i, and the difference is zero to the last bit: live demo
“Stationary and reversible are the same thing.” — no. Stationarity balances flow into and out of each state in aggregate; detailed balance balances it edge by edge. Every reversible chain is stationary, but a directed cycle can be stationary while flowing strictly one way — stationary with no detailed balance. cited
Reversibility is a time-symmetry: a movie of the chain at equilibrium plays the same forwards and backwards. It is the self-inverse — the palindrome — showing up in probability rather than in bits. Boltzmann 1872
On the canonical compiler a Metropolis chain built for π = (0.2, 0.3, 0.5) satisfies detailed balance exactly — every forward flux equals its reverse, difference zero: