Prediction and Design
The Annals of Statistics · 1987 · Volume 15 · Issue 1
Abstract
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.
Keywords
60G15, 62K05, 62M20, Bayesian models, design, Gaussian fields, Markov processes, Markov property, prediction
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