by

Consider the problem of consistent Bayesian estimation of a stationary “k’th-order Markov process ” on a finite state space, when the parameter k is itself unknown, as well as the transition probabilities for each value of k. First, I show that if k has a known upper bound, then on a single realization of the process the posterior probabil-ity measures on the parameter space converge weakly to a probability measure wit