Training and Research Focus
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 Complex Models (SIM)
The new demands of scientific research and the availability of vast data sets have required statisticians to revisit and reformulate the foundations of their discipline so that theory and methods become scalable to modern data.
The new demands of scientific research and the availability of vast data sets have required statisticians to revisit and reformulate the foundations of their discipline so that theory and methods become scalable to modern data.
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.
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 Graphical Models (MLP)
Over recent decades a mutual understanding of the rich symbioses between statistics and machine learning methodologies has developed and researchers have now begun to exploit these relationships. One of the key areas of such exchange is in probabilistic graphical modelling.
Over recent decades a mutual understanding of the rich symbioses between statistics and machine learning methodologies has developed and researchers have now begun to exploit these relationships. One of the key areas of such exchange is in probabilistic graphical modelling.
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.
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.
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.
