We develop a Bayesian approach for monitoring and graphically exploring a process mean and informing decisions related to process adjustment. We assume a rather general model, in which the observations are represented as a process mean plus a random error term. In contrast to previous work on Bayesian methods for monitoring a mean, we allow any Markov model… (More)