Abstract An approach to the choice of experiment to aid prediction is proposed in a Bayes decision-theoretic framework.
Estimation is included as a special case.
We characterize coherent design criteria which are functions of the dispersion matrix, and their associated decision problems. Connexions are established with the classical approach to optimal design theory for the normal linear model, thus making its optimization techniques available, for suitable problems, in this new setting. Keywords Amenable, Bayesian decision theory, coherent, graceful, charming, dispersion criterion, optimal design, optimality criterion, prediction, proper scoring rule, uncertainty function, viable. Also as UCL Research report, no 167. This page is maintained by the Department of Actuarial Science and Statistics .