Supervisor: Dr Silvia Liverani
Project description:
Many models assume that observations are obtained independently of each other. However, distance between observations can be a source of correlation, which needs to be accounted for in any model. For example, pollution has a spatial smooth pattern and measurements close in space are likely to be very similar. Spatial models therefore have to take into account the spatial autocorrelation in datasets in order to separate the general trend (usually depending on some covariates) from the purely spatial random variation.
This project will focus on developing and applying Bayesian spatial and spatio-temporal modelling techniques to enhance our understanding of the association between variables and prediction. These methods can be applied to health or other areas, such as biodiversity. Regarding the latter, there will be an opportunity to work with Kew Gardens.
Further information:
How to apply
Entry requirements
Fees and funding