The Centre is focused on a new type of training which has been customized to a new type of graduate statistician in statistical methodology and computation and that is scalable to big data. We bring a new focus on training for research, by teaching directly from the scientific literature.
Statistical Inference in
Multivariate Stochastic Processes (MSP)
A substantial number of inferential environments evolve dynamically in time or space, or both, often under stochastic control. A wide range of applied probabilistic and statistical methods are currently being developed to address these needs.
Machine Learning and Probabilistic
Bayesian Analyses for Complex Structural Information (BAC)
The recent surge in Bayesian methodologies merges the now well understood tools of probabilistic reasoning with stochastic computational and statistical inference. Current research frontiers further develop this relationship to apply to an ever increasing domain of application where essential contextual structural information can be properly coded as part of an extensive data-analysis exercise.
Stochastic Computation for Intractable Inference (SCI)
Many recent advances in statistical modelling have only been made possible by the dramatic progress in techniques which admit the fast analysis of probabilistic and statistical models. These methods are being increasingly customized to the needs of different model classes, some of which are outlined above.
All these important themes as well as major connections with industrial and scientific application domains are essential to your training.
On completion of your time at the CDT you will be able to:
- Understand the inferential challenges of 21st Century data-intensive environments;
- Appreciate the major scientific, industrial and societal applications of data -intensive statistics;
- Demonstrate a critical understanding of a wide variety of statistical methods;
- Understand the foundations of statistical inference and probabilistic models;
- Design and implement a study from data capture through to analysis and critical evaluation of evidence;
- Demonstrate self -motivation and originality in technical problem solving;
- Communicate effectively, both in writing and orally;
- Work effectively in teams, including across disciplines;
- Appreciate central issues in reproducible research, innovation, and entrepreneurship.