Bayesian Methods and Computation
Bayesian methods offers a consistent way of characterising uncertainty in statistical inference while incorporating multiple sources of information, perhaps including prior information. In many cases such methods are computationally demanding. The work carried out within the Bayesian Methods and Computation theme focuses on t he development and application of highly efficient approaches to performing Bayesian inference. This involves the development of state-of-the-art methodology, as well as tackling the computational issues that arise from their practical implementation, in particular by using efficient computational methods and/or large-scale cluster computing and other highly parallelised hardware.
Much of the research within this theme is directly motivated by real-world applications, in finance, retail and engineering, and by important scientific questions arising in astronomy and the medical and life sciences. Our work is therefore driven through strong links with our many scientific and industrial collaborators, and our members are frequently invited to give high profile seminars and talks at leading institutions, academic societies and companies throughout the world.
Invited Talks / Keynote Presentations:
- Young (2011) "Achieving accuracy and correctness in parametric inference" ERCIM, London
- Heard (2011) "Real Time Anomaly Detection with Applications in Dynamic Networks" Hierarchical Models and Markov Chain Monte Carlo, Hersonissos, Crete.