Computational Statistics
Providing an essential link connecting data to statistical models and learning.
Research group focus
Our group has diverse expertise in producing methods for large or complex datasets, as well as in their performance analysis. Some key interests include:
- Monte Carlo methods (e.g., non-reversible MCMC methods and sequential Monte Carlo)
- Bayesian methods and their approximation
- Methods for network-valued or heavy-tailed data
- Emulation of complex statistical models
- Applications in engineering, astronomy, biology and sports science
Key collaborators
Group members contribute to two large collaborative research programmes:
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PINCODE (EPSRC), led from the University of Warwick
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OCEAN (ERC Synergy Grant), a collaboration between the University of Warwick, École Polytechnique, Université Paris Dauphine–PSL and UC Berkeley
Recently completed:
- Harnessing the power of Stein discrepancies in Bayesian computation (EPSRC, 2022–2026), led from Newcastle University