The Chain Event Graph (CEG) is a new class of graphical model, first introduced in Smith and Anderson [2008], which is derived from a probability tree by merging vertices whose associated conditional probabilities are the same. It is proving to be a useful framework for modelling asymmetric problems and further generalises the Bayesian Network (BN), by allowing for context-specific dependence structures between the variables of the problem. This thesis provides a first demonstration of the value of using the CEG in real-world applications and the new techniques developed here are motivated by problems that arise from two health studies; the Christchurch Health and Development Study (CHDS) and the UK Cerebral Palsy (UKCP) Cohort Study. A direct comparison of the BN and CEG on the CHDS demonstrates that the CEG can lead to significantly higher scoring models than the BN and further that it enables additional conclusions to be drawn on the health study directly from the topology of its graph. An extension of the CEG, the Ordinal CEG, is developed in this thesis, which further enhances the graphical representation of the CEGs for studies with a binary outcome. Motivated by the UKCP this thesis further investigates how missing data structures can be explicitly represented by a CEG and how its graph can consequently provide a precise understanding of the influence of missingness. Finally, a dynamic version of the CEG is developed and it is demonstrated how this new class of models generalises the Dynamic BN and is further closely linked to (semi-) Markov processes. The expressiveness of this model is illustrated through a fictional example.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:618983 |
| Date | January 2014 |
| Creators | Barclay, Lorna M. |
| Publisher | University of Warwick |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Source | http://wrap.warwick.ac.uk/63223/ |
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