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Emulation of random output (stochastic) models: theory and application

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If you have a question about this talk, please contact Michael Sweeting.

In this talk we will briefly introduce the concept of emulation – the construction of a surrogate model – which is typically used to speed up (Bayesian) analysis using deterministic computer models, as presented in the MUCM project. Most existing approaches to emulation assume that the computer simulator is deterministic, however increasingly computer simulators are becoming stochastic. This typically arises from some unknown or random interactions within the simulator which induce random outputs. We will discuss how to build emulators of stochastic simulators, focusing on heteroscedastic modelling using Gaussian processes. In particular we will address the experimental design issue when developing stochastic emulators. We will present results from our new Fisher information based design approach, and show the application of this to a relatively simple two vector rabies model. We will conclude with a discussion of interesting future directions for building emulators for stochastic simulators.


This talk is part of the MRC Biostatistics Unit Seminars series.

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