Asymptotic Bayes Criteria for Nonparametric Response Surface Design

Abstract

This paper deals with Bayesian design for response surface prediction when the prior may be finite or infinite dimensional, the design space arbitrary. In order that the resulting problems be manageable, we resort to asymptotic versions of D-, G- and A-optimality. Here the asymptotics stem from allowing the error variance to be large. The problems thus elicited have strong game-like characteristics. Examples of theoretical solutions are brought forward, especially when the priors are stationary processes on an interval, and we give numerical evidence that the asymptotics work well in the finite domain.