# A Separation Theorem for Chain Event Graphs

arXiv: https://arxiv.org/abs/1501.05215 · HTML: https://mlsanity.com/paper/1501.05215/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jan 2015.

## Built on by (most cited first, 2 of 2)

- [Staged trees and asymmetry-labeled DAGs](https://mlsanity.com/paper/2108.01994/) (2021)
- [Structural Learning of Simple Staged Trees](https://mlsanity.com/paper/2203.04390/) (2022)

## Abstract (arXiv)

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.