Here you can find details of some ongoing projects in our group.

Harnessing the power of Stein discrepancies in Bayesian computation (2022-2026)

Chris Oates is leading an EPSRC-funded project on exploiting Stein discrepancies in Bayesian computation. The project aims to accelerate the process of fitting models to data by developing novel computational methodology that is more efficient than the current state-of-the-art. The major technical advance that underpins this research is “Stein discrepancy”, which enables an optimisation-centric perspective on numerical approximation of the posterior distribution, to which powerful optimisation techniques can be employed, making it possible to fit more appropriate and sophisticated models to data. This will, in turn, add value in the diverse application domains in which computational models, and the inferences and predictions that they produce, are employed. Two such applications are considered: developing patient-specific multi-physics models of the human heart, with a view to personalised treatment, and predicting the mechanical properties of structures built using novel techniques and novel materials.