The Statistics and Probability Research Group is formed of four subgroups.
Applied and Theoretical Probability
The Applied and Theoretical Probability subgroup has interests ranging from the theoretical foundations of random objects in pure mathematics through to modern practical applications such as developments in computing.
- Diffusion processes on manifolds
- Limit theorems on Riemannian manifolds with or without singularities
- Resource allocation, through restless bandits
- Stochastic optimal control
- Stochastic numerics and modelling
- Theory of Markov renewal processes and semi-Markov processes
Computational and Theoretical Statistics
This subgroup is primarily concerned with the science of reasoning under uncertainty.
We collaborate with the Centre for Plant Integrative Biology, on analysing large “-omics” data sets, for example for gene network inference.
Analysis of computer experiments and uncertainty quantification
Analysis of large high-dimensional data sets
Bayesian computational statistics
Bootstrap and empirical likelihood methods
Extreme value analysis
Inference for dynamical models defined by stochastic and ordinary differential equations
Spatial and spatio-temporal modelling
Statistics for directional data and other non-Euclidean data types
Epidemic Modelling uses mathematics to study the mechanisms underlying the spread of infectious diseases such as influenza, foot-and-mouth disease, AIDS and Ebola. The subgroup has particular expertise in stochastic models.
- Modelling epidemics with a structured underlying population (e.g. households, random networks, hospital wards and farms)
- Rigorous analysis of stochastic epidemic models
- Analysis of disease outbreak data
- Bayesian computational methods for infectious disease data
- Assessing if future disease outbreaks can be prevented
- Analysis of the effect of intervention strategies such as vaccination
The subgroup has strong collaborative links with institutions such as Public Health England, leading UK Hospitals such as Guy's and St Thomas' Hospital in London, and The University of Nottingham’s School of Veterinary Medicine and Science.
Shape and Object Data Analysis
Shape analysis is a thriving research field driven by wide applications in areas such as bioinformatics, medicine, biology and engineering. An even broader research area is Object Data Analysis which includes the analysis of data objects' such as images, shapes, trees, dynamical systems and functional data. The impact of our current Shape and Object Data Analysis research includes improving the recognition of disease from medical images.