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 asymmetric conditional independence statements 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 that include the risk of childhood hospitalization and the efficacy of a flu vaccine.
Citation
Lorna M. Barclay. Rodrigo A. Collazo. Jim Q. Smith. Peter A. Thwaites. Ann E. Nicholson. "The dynamic chain event graph." Electron. J. Statist. 9 (2) 2130 - 2169, 2015. https://doi.org/10.1214/15-EJS1068
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