In a general setting we solve the following inverse problem: Given a positive operators R, acting on measurable functions on a fixed measure space (X, B X ), we construct an associated Markov chain. Specifically, starting with a choice of R (the transfer operator), and a probability measure µ 0 on (X, B X ), we then build an associated Markov chain T 0 , T 1 , T 2 , . . ., with these random variables (r.v) realized in a suitable probability space (Ω, F , P), and each r.v. taking values in X, and with T 0 having the probability µ 0 as law. We further show how spectral data for R, e.g., the presence of R-harmonic functions, propagate to the Markov chain. Conversely, in a general setting, we show that every Markov chain is determined by its transfer operator. In a range of examples we put this correspondence into practical terms: (i) iterated function systems (IFS), (ii) wavelet multiresolution constructions, and (iii) IFSs with random control. Our setting for IFSs is general as well: a fixed measure space (X, B X ) and a system of mappings τ i , each acting in (X, B X ), and each assigned a probability, say p i which may or may not be a function of x. For standard IFSs, the p i 's are constant, but for wavelet constructions, we have functions p i (x) reflecting the multi-band filters which make up the wavelet algorithm at hand. The sets τ i (X) partition X, but they may have overlap, or not. For IFSs with random control, we show how the setting of transfer operators translates into explicit Markov moves: Starting with a point x ∈ X, the Markov move to the next point is in two steps, combined yielding the move from T 0 = x to T 1 = y, and more generally from T n to T n+1 . The initial point x will first move to one of the sets τ i (X) with probability p i , and once there, it will choose a definite position y (within τ i (X)), now governed by a fixed law (a given probability distribution). For Markov chains, the law is the same in each move from T n to T n+1 .