A composite likelihood approach to computer model calibration using high-dimensional spatial data

TL;DR

This paper introduces a computationally efficient composite likelihood approach for calibrating complex computer models with high-dimensional spatial data, addressing challenges in environmental science applications.

Contribution

It develops a novel Bayesian calibration method using composite likelihood and provides asymptotic adjustments for accurate uncertainty quantification.

Findings

Effective calibration of climate models demonstrated

Computational efficiency achieved for high-dimensional data

Adjusted posterior distributions improve uncertainty estimates

Abstract

Computer models are used to model complex processes in various disciplines. Often, a key source of uncertainty in the behavior of complex computer models is uncertainty due to unknown model input parameters. Statistical computer model calibration is the process of inferring model parameter values, along with associated uncertainties, from observations of the physical process and from model outputs at various parameter settings. Observations and model outputs are often in the form of high-dimensional spatial fields, especially in the environmental sciences. Sound statistical inference may be computationally challenging in such situations. Here we introduce a composite likelihood-based approach to perform computer model calibration with high-dimensional spatial data. While composite likelihood has been studied extensively in the context of spatial statistics, computer model calibration using composite likelihood poses several new challenges. We propose a computationally efficient approach for Bayesian computer model calibration using composite likelihood. We also develop a methodology based on asymptotic theory for adjusting the composite likelihood posterior distribution so that it accurately represents posterior uncertainties. We study the application of our new approach in the context of calibration for a climate model.