A clustered Gaussian process model for computer experiments

TL;DR

This paper introduces a clustered Gaussian process model that segments data into clusters for improved emulation of computer experiments, addressing stationarity and scalability issues, and demonstrates its effectiveness through simulations and real data.

Contribution

The paper proposes a novel clustered Gaussian process model with an efficient EM algorithm, enhancing scalability and interpretability for computer experiment emulation.

Findings

Smaller mean square errors compared to competitors

Competitive computation time

Provides insights through cluster discovery

Abstract

A Gaussian process has been one of the important approaches for emulating computer simulations. However, the stationarity assumption for a Gaussian process and the intractability for large-scale dataset limit its availability in practice. In this article, we propose a clustered Gaussian process model which segments the input data into multiple clusters, in each of which a Gaussian process model is performed. The stochastic expectation-maximization is employed to efficiently fit the model. In our simulations as well as a real application to solar irradiance emulation, our proposed method had smaller mean square errors than its main competitors, with competitive computation time, and provides valuable insights from data by discovering the clusters. An R package for the proposed methodology is provided in an open repository.