Chain event graphs : theory and application

This thesis is concerned with the Graphical model known as the Chain Event Graph (CEG) [1][60][61], and develops the theory that appears in the currently published papers on this work. Results derived are analogous to those produced for Bayesian Networks (BNs), and I show that for asymmetric problems the CEG is generally superior to the BN both as a representation of the problem and as an analytical tool. The CEG is designed to embody the conditional independence structure of problems whose state spaces are asymmetric and do not admit a natural Product Space structure. In this they differ from BNs and other structures with variable-based topologies. Chapter 1 details researchers' attempts to adapt BNs to model such problems, and outlines the advantages CEGs have over these adaptations. Chapter 2 describes the construction of CEGs. In chapter 3I create a semantic structure for the reading of CEGs, and derive results expressible in the form of context-specific conditional independence statements, that allow us to delve much more deeply into the independence structure of a problem than we can do with BNs. In chapter 4I develop algorithms for the updating of a CEG following observation of an event, analogous to the Local Message Passing algorithms used with BNs. These are more efficient than the BN-based algorithms when used with asymmetric problems. Chapter 5 develops the theory of Causal manipulation of CEGs, and introduces the singular manipulation, a class of interventions containing the set of interventions possible with BNs. I produce Back Door and Front Door Theorems analogous to those of Pearl [42], but more flexible as they allow asymmetric manipulations of asymmetric problems. The ideas and results of chapters 2 to 5 are summarised in chapter 6.