Robert B. Gramacy

TL;DR: In this paper, a non-stationary modeling methodologies that couple stationary Gaussian processes with treed partitioning is presented. But this method is not applicable to the design of a rocket booster.

TL;DR: This article explores nonstationary modeling methodologies that couple stationary Gaussian processes with treed partitioning and shows that this approach is effective in other arenas as well.

TL;DR: A family of local sequential design schemes that dynamically define the support of a Gaussian process predictor based on a local subset of the data are derived, enabling a global predictor able to take advantage of modern multicore architectures.

TL;DR: This study provides an introductory overview of several methods for analyzing large spatial data and describes the results of a predictive competition among the described methods as implemented by different groups with strong expertise in the methodology.

TL;DR: In this article, the results of a predictive competition among the described methods as implemented by different groups with strong expertise in the methodology have been presented, and each group then wrote their own implementation of their method to produce predictions at the given location and each which was subsequently run on a common computing environment.