# An N Time-Slice Dynamic Chain Event Graph

arXiv: https://arxiv.org/abs/1808.05726 · HTML: https://mlsanity.com/paper/1808.05726/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2018.

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

- [Beyond Conjugacy for Chain Event Graph Model Selection](https://mlsanity.com/paper/2211.03427/) (2022)
- [stCEG: An R Package for Modelling Events over Spatial Areas Using Chain Event Graphs](https://mlsanity.com/paper/2507.06726/) (2025)

## Abstract (arXiv)

The Dynamic Chain Event Graph (DCEG) is able to depict many classes of discrete random processes exhibiting asymmetries in their developments and context-specific conditional probabilities structures. However, paradoxically, this very generality has so far frustrated its wide application. So in this paper we develop an object-oriented method to fully analyse a particularly useful and feasibly implementable new subclass of these graphical models called the N Time-Slice DCEG (NT-DCEG). After demonstrating a close relationship between an NT-DCEG and a specific class of Markov processes, we discuss how graphical modellers can exploit this connection to gain a deep understanding of their processes. We also show how to read from the topology of this graph context-specific independence statements that can then be checked by domain experts. Our methods are illustrated throughout using examples of dynamic multivariate processes describing inmate radicalisation in a prison.