Bayesian statistics is a subset of the field of statistics where some initial belief is expressed in terms of a statistical distribution. The research conducted in this area at Kent is mainly on Bayesian variable selection, Bayesian model fitting, Bayesian nonparametric methods, Monte Carlo Markov chain methods, and applications in areas including biology, economics, finance and engineering.
Modern societies produce huge amounts of data. However, this information is only useful if we can analyse it and gain practical insights. Data science combines powerful computing technology, sophisticated statistical methods, and expert subject knowledge to carry out this analysis.
The idea of using computer-based learning to solve scientific problems has inspired a surge of activity in this area over a decade. The members of the Machine Learning research team at Kent focus on both theoretical research and its applications. As a result of our research, better (computer) programs have been developed and applied to various real applications.
As the environment changes, we see corresponding effects in the behaviour of wild animals and plants. Members of the Statistical Ecology at Kent group (SE@K) analyse ecological data to try to describe and better understand these changes.
The Statistics group, based within the School of Mathematics, Statistics and Actuarial Science (SMSAS) is recognised internationally as a centre of excellence in our field of research. The 2014 Research Excellence Framework (REF) rated 72% of SMSAS research output as International Quality or above, and the School is in the top 25 in the UK when considered on weighted GPA.
Some of our current research projects
Our regular research seminars
