Prediction based on response surface data obtained with random blocking

Summary

In many experiments, a main purpose is prediction of a future response, future to the data obtained from the experiment. Frequently, such experiments have to be run in blocks where the block effects are random. In this paper, we describe a Bayesian approach to the problem of prediction, given data which have been obtained from a response surface design with random blocking. The predictive distribution involves a posterior distribution which is not available in closed form, and we outline a Gibbs sampling procedure to carry out the determination of the needed predictive distribution.