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School of Mathematical Sciences

PSD - Dr Silvia Liverani

Bayesian Spatio-temporal Modelling 

Supervisor: Dr Silvia Liverani

Project description:

Dr Silvia Liverani's research interests are in Bayesian methods and their applications. In particular, her work focuses on Bayesian clustering models and Dirichlet process clustering. She is also conducting research on several developments for spatial-temporal models and survival response models. She is happy to supervise PhD projects on diverse areas of application including biodiversity, genetics, epidemiology, road traffic accidents, football and environmental health.

Examples of projects that she is happy to supervise are the following.

(1) Spatio-temporal modelling for biodiversity data.
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 models. For example, pollution has a spatial smooth pattern and measurements close in space are likely to be very similar. Spatial models will therefore have to consider any 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 predict (1) plant species that are the cause for concern, for example species at risk of extinction or of being invasives and (2) areas in need of protection in the face of climate change, changing land use (especially agriculture) and pollution. One statistical challenge that arises in this study is that the data available are at different resolutions. Advanced methods are required to model misaligned spatial and spatio-temporal data. We will leverage recent work by Dr Silvia Liverani on Bayesian methods for misaligned areal data, and extend them to suit meet the needs of this research challenge in the study and understanding of biodiversity.

(2) Clustering different types of data
Many different kinds of data can be used with algorithms of clustering. The data can be like binary data, categorical and interval-based data, or a mixture of these types of data. However, when it is a mixture of different types of data, it is not clear how this affects the results. This PhD project will study this for Dirichlet process mixture models, using and extending the R package PReMiuM.

Further information: 
How to apply 
Entry requirements 
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