The Restricted Additive Schwarz (RAS) method is adapted to the problem of computing the stationary probability distribution vector of large, sparse, irreducible stochastic matrices. Inexact and two-level variants are also considered, as well as acceleration by Krylov subspace methods. The convergence properties are analyzed and extensive numerical experiments aimed at assessing the effect of varying the number of subdomains and the amount of overlap are discussed.