On sequential designs in nonlinear problems

Abstract

In this paper we study the consistency of parameter estimators in sequential nonlinear design. Our main tool is a recursion which holds for the sequence of posterior variances. Under mild conditions, we establish the strong consistency of Bayes estimators in stochastic regression models which cover a broad range of nonlinear problems. Four examples from nonlinear sequential design are discussed.