Bayesian hierarchical modelling (BHM) is one of the most powerful modern statistical techniques. It provides a unifying framework for dealing with a diversity of sources of complexity arising from the structure (e.g. dependence) of the data and its associated measurement process. Hierarchical model building strategy involves defining latent unobserved quantities of interest which are organised into a number of levels with distinct interpretations and building probabilistic between the latent quantities and the data. Bayesian hierarchical models coupled with efficient computational tools have been successfully used in a very wide range of application areas (e.g epidemiology, social sciences, education, geography, environmental sciences, biomedicine, political sciences).

By its generic character, this modelling strategy has the potential to bring together scientists from a wide range of disciplines across the University. Computationally, it also raises a number of algorithmic challenges which could provide useful topics for interactions.

This Research Theme is led by Prof Mark Girolami and Prof Sylvia Richardson.

If you are interested in attending, please contact the coordinator (coordinator@bigdata.cam.ac.uk) with a brief motivation on your research interest; this event is not intended for students or industry.