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Durham University

Department of Mathematical Sciences


This week's seminars

Stats4Grads: Multilevel Emulation of Stochastic Computer Codes

Presented by Jack Kennedy, Newcastle University

19 February 2020 13:00 in CM105

Increasingly, stochastic computer models are being used in science and engineering to predict complex phenomena. Such stochastic models are implemented as computer simulators which may takes minutes, hours or even days to run. A common approach to alleviate this problem is to build a statistical surrogate model, known as an emulator. Emulators of stochastic computer models should accurately predict the mean response surface of the simulator but also their level of noise. A particularly flexible approach is to emulate the stochastic simulator via a heteroscedastic Gaussian process.

Many complex simulators can be run at different levels of accuracy and hence different computational cost. Although the cheapest to run simulators will be inaccurate, they may be informative for more expensive, but slower, runs of the computer simulator. We present a method to incorporate cheap simulator runs into a heteroscedastic GP emulator.

Contact for more information

See the Stats4Grads page for more details about this series.