Structuring conditional relationships in influence diagrams
Operations Research
Communications of the ACM
A tutorial on learning with Bayesian networks
Learning in graphical models
The Art of Causal Conjecture
Probabilistic Networks and Expert Systems
Probabilistic Networks and Expert Systems
Conditional independence and chain event graphs
Artificial Intelligence
Exploiting contextual independence in probabilistic inference
Journal of Artificial Intelligence Research
Causal analysis with Chain Event Graphs
Artificial Intelligence
Context-specific independence in Bayesian networks
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Refining a Bayesian Network using a Chain Event Graph
International Journal of Approximate Reasoning
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Chain event graphs are graphical models that while retaining most of the structural advantages of Bayesian networks for model interrogation, propagation and learning, more naturally encode asymmetric state spaces and the order in which events happen than Bayesian networks do. In addition, the class of models that can be represented by chain event graphs for a finite set of discrete variables is a strict superset of the class that can be described by Bayesian networks. In this paper we demonstrate how with complete sampling, conjugate closed form model selection based on product Dirichlet priors is possible, and prove that suitable homogeneity assumptions characterise the product Dirichlet prior on this class of models. We demonstrate our techniques using two educational examples.