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Paper No. 14-04

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LM Barclay, RA Collazo, JQ Smith, PA Thwaites and AE Nicholson

The Dynamic Chain Event Graph

Abstract: In this paper we develop a formal dynamic version of Chain Event Graphs (CEGs), a particularly expressive family of discrete graphical models. We demonstrate how this class links to semi-Markov models and provides a convenient generalization of the Dynamic Bayesian Network (DBN). In particular we develop a repeating time-slice Dynamic CEG providing a useful and simpler model in this family. We demonstrate how the Dynamic CEG’s graphical formulation exhibits the essential qualitative features of the hypotheses it embodies and also how each model can be estimated in a closed form enabling fast model search over the class. The expressive power of this model class together with its estimation is illustrated throughout by a variety of examples including ones describing childhood hospitalization and the efficacy of a flu vaccine.

Keywords: Chain Event Graphs, Markov Processes, Probabilistic Graphical Models, Dynamic Bayesian Networks.