Relative error stochastic kriging

Abstract

We use stochastic kriging to build predictors with bounded relative error over the design space. We propose design strategies that guide sequential algorithms with and without adaptation to the data to make allocation and stopping decisions such that a prespecified relative precision is realized with some confidence. We also present an empirical evaluation of the proposed design strategies.