The Applied Probability and Theoretical Statistics research group is active in the development of new statistical methodologies for inference in stochastic systems, as well as in adapting and extending existing methods and computational algorithms. Primary interests are in developing the theoretical properties of techniques used for both classical and Bayesian statistics and in the study of applied probability models. Particular focus lies in inference procedures for complex data structures, high-dimensional data and non-standard stochastic models. The group is active in advancing the theory and methodology of Monte Carlo methods and is particularly motivated to the development of highly accurate inference methods for a range of applications, including spatial-temporal modelling, mathematical finance and medicine.