In various settings, the observation of a stochastic process at a finite number of locations leads to natural prediction and design questions. General problems of this type are introduced and then related to specific areas of application. A class of processes called G-MAPs is studied with reference to their predictive and other behavior. These processes include many familiar ones and, through being tied to Markov processes, allow a fresh view of prediction. Among other things, G-MAPs stand as reasonably workable possibilities for Bayesian priors in some complex contexts.
"Prediction and Design." Ann. Statist. 15 (1) 1 - 19, March, 1987. https://doi.org/10.1214/aos/1176350247