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.

翻译：在计算机代码中采用数学模型的计算费用昂贵模拟器,通常使用统计模拟器进行近似。我们为通过链条、系列或模拟器网络进行最佳模拟的系统开发并评估新的模拟方法。我们利用Bayes线性框架,将部件模拟器的统计模拟器连接起来,以明确说明任意大型网络中模型之间联系引起的模拟器输入不确定性。我们展示了这些方法的优点,与使用合成模拟器网络的单一模拟器相比,我们有许多例子,包括激励流行病学模拟器链,以模拟空气传播传染病的影响。