2015/01/21 by Peter Thwaites, Thwaites, Peter A., Jim Q. Smith +1
Biochemistry, Genetics and Molecular Biology · Computer Science · #62F15 (Primary) #68T37 (Secondary) #Artificial Intelligence (cs.AI) #Bayesian Modeling and Causal Inference #Cognitive Science and Mapping #FOS: Computer and information sciences #Gene Regulatory Network Analysis #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.1501.05215
openalex publication_date 2015/01/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Bayesian Networks (BNs) are popular graphical models for the representation of statistical problems embodying dependence relationships between a number of variables. Much of this popularity is due to the d-separation theorem of Pearl and Lauritzen, which allows an analyst to identify the conditional independence statements that a model of the problem embodies using only the topology of the graph. However for many problems the complete model dependence structure cannot be depicted by a BN. The Chain Event Graph (CEG) was introduced for these types of problem. In this paper we introduce a separation theorem for CEGs, analogous to the d-separation theorem for BNs, which likewise allows an analyst to identify the conditional independence structure of their model from the topology of the graph.