A discrete-time Markov chain is a stochastic model describing random sequences of state transitions where the conditional probability distribution of the future state depends exclusively upon the present state, satisfying the memoryless Markov property. Transitions are parameterized by a row-stochastic probability matrix P. Under irreducible and aperiodic conditions, the state probability distribution converges toward a unique invariant stationary distribution vector π.