Bayesian treed Gaussian process models with an application to computer modeling

TL;DR

This paper introduces a nonstationary modeling approach combining Gaussian processes with treed partitioning, demonstrated on rocket booster data and other applications, enhancing modeling flexibility and efficiency.

Contribution

It develops a novel Bayesian treed Gaussian process methodology for nonstationary modeling, with detailed computational strategies and broad application demonstrations.

Findings

Effective modeling of nonstationary data using treed Gaussian processes.

Successful application to rocket booster simulation data.

Versatile approach applicable to various complex modeling scenarios.

Abstract

Motivated by a computer experiment for the design of a rocket booster, this paper explores nonstationary modeling methodologies that couple stationary Gaussian processes with treed partitioning. Partitioning is a simple but effective method for dealing with nonstationarity. The methodological developments and statistical computing details which make this approach efficient are described in detail. In addition to providing an analysis of the rocket booster simulator, our approach is demonstrated to be effective in other arenas.