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Computationally expensive simulators, implementing mathematical models incomputer codes, are commonly approximated using statistical emulators. Wedevelop and assess novel emulation methods for systems best modelled via achain, series or network of simulators. Using a Bayes linear framework, we linkstatistical emulators of the component simulators to explicitly account for thesimulator input uncertainty induced by links between models in arbitrarilylarge networks. We demonstrate the advantages of these methods compared to useof a single emulator of the composite simulator network for a variety ofexamples, including the motivating epidemiological simulator chain to model theimpact of an airborne infectious disease.

Bayes Linear Emulation of Simulator Networks