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.
The Cambridge Centre for Data-Driven Discovery (C2D3) brings together researchers and expertise from across the academic departments and industry to drive research into the analysis, understanding and use of data science.
The Centre is funded by a series of collaborations with partners in business and industry which have an interest in using data science for the benefit of their customers and their organisations. Our founding partner is Aviva, the UK’s leading insurance company. We work with industrial partners to build a portfolio of collaborative research projects, provide professional development opportunities for their own staff and access to the full breadth and depth of the University’s talent pool in the area of data science.
With unprecedented access to increasing volumes of data, our research ranges from the underlying fundamentals in mathematics and computer science, to data science applications across all six University Schools of Arts and Humanities, Biological Sciences, Clinical Medicine, Humanities and Social Sciences, Physical Sciences, and Technology.
In parallel, our research addresses important issues around law, ethics and economics, in order to apply data science to solve challenging problems for society.
C2D3 supports collaboration and knowledge transfer in this growing field.
Cambridge Centre for 
