This course provides an introduction to Bayesian Hierarchical Models, with the aim of providing an interactive experience
for students and researchers from a variety of fields and to allow them to experience state of the art statistical methodology
and its application. It will cover modelling relationships in both space and time with particular focus on fitting complex
models to big data. The course covered both theoretical and applied examples, the latter specifically through practical
‘hands-on’ computer sessions, using R and R-INLA, in which participants will be guided through the analyses of real data
with both temporal and spatial structure.

The course will be delivered at the Latin American Congress of Probability and Mathematical Statistics (CLAPEM) in
Universidad de Costa Rica between 6th-9th December 2016. The presenters are Gavin Shaddick, Millie Green and
Matthew Thomas.

The slides, practical sheets, and other relevant resources can be found below.