Sam Jackson (Durham University) – Efficient Emulation Utilising Known Boundary Information

Bayesian emulation, and more generally Gaussian process models, have been successfully applied across a wide variety of scientific disciplines. This is both in the context of efficiently analysing computationally intensive models, as well as general statistical models for inference and prediction of response for new predictors given a training dataset. In this talk, we introduce emulators as fast statistical approximators, providing a predicted value at any input, along with a corresponding measure of uncertainty. We then proceed to discuss developments of Known Boundary Emulation (KBE) strategies which utilise the fact that, for many computer models, there exist hyperplanes in the input parameter space for which the model output can be evaluated far more efficiently. For example, this may be because the response is known at such inputs; or in the context of a computer model, such inputs may yield an analytical solution or the potential for application of a much simpler, more efficient, numerical solver. We demonstrate how information on these known hyperplanes can be incorporated into the emulation process via analytical update, thus involving no additional computational cost, before illustrating our techniques on a scientifically relevant and high-dimensional systems biology model of hormonal crosstalk in the roots of an Arabidopsis plant.