Bayes Linear Emulation of Simulator Networks

TL;DR

This paper introduces a Bayesian linear emulation approach for complex networks of interconnected simulators, improving uncertainty quantification and efficiency over traditional single-emulator methods.

Contribution

It develops a novel Bayes linear framework to explicitly model input uncertainty in networked simulators, enhancing emulation accuracy and scalability.

Findings

Outperforms single-emulator approaches in complex networks

Effectively models uncertainty propagation in simulator chains

Demonstrated on epidemiological disease spread model

Abstract

Computationally expensive simulators, implementing mathematical models in computer codes, are commonly approximated using statistical emulators. We develop and assess novel emulation methods for systems best modelled via a chain, series or network of simulators. Using a Bayes linear framework, we link statistical emulators of the component simulators to explicitly account for the simulator input uncertainty induced by links between models in arbitrarily large networks. We demonstrate the advantages of these methods compared to use of a single emulator of the composite simulator network for a variety of examples, including the motivating epidemiological simulator chain to model the impact of an airborne infectious disease.