# Beyond Conjugacy for Chain Event Graph Model Selection

arXiv: https://arxiv.org/abs/2211.03427 · HTML: https://mlsanity.com/paper/2211.03427/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 39 references that are in the map.
- First posted on arXiv 7 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [Rank-normalization, folding, and localization: An improved widehatR for assessing convergence of MCMC](https://mlsanity.com/paper/1903.08008/) (2019)
- [Hierarchical Clustering: Objective Functions and Algorithms](https://mlsanity.com/paper/1704.02147/) (2017)
- [Context-Specific Independence in Bayesian Networks](https://mlsanity.com/paper/1302.3562/) (2013)
- [The R Package stagedtrees for Structural Learning of Stratified Staged Trees](https://mlsanity.com/paper/2004.06459/) (2020)
- [Workflow Techniques for the Robust Use of Bayes Factors](https://mlsanity.com/paper/2103.08744/) (2021)
- [A comparative study of divisive hierarchical clustering algorithms](https://mlsanity.com/paper/1506.08977/) (2015)
- [Constructing a Chain Event Graph from a Staged Tree](https://mlsanity.com/paper/2006.15857/) (2020)
- [An N Time-Slice Dynamic Chain Event Graph](https://mlsanity.com/paper/1808.05726/) (2018)
- [Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling](https://mlsanity.com/paper/2202.12352/) (2022)

## Abstract (arXiv)

Chain event graphs are a family of probabilistic graphical models that generalise Bayesian networks and have been successfully applied to a wide range of domains. Unlike Bayesian networks, these models can encode context-specific conditional independencies as well as asymmetric developments within the evolution of a process. More recently, new model classes belonging to the chain event graph family have been developed for modelling time-to-event data to study the temporal dynamics of a process. However, existing model selection algorithms for chain event graphs and its variants rely on all parameters having conjugate priors. This is unrealistic for many real-world applications. In this paper, we propose a mixture modelling approach to model selection in chain event graphs that does not rely on conjugacy. Moreover, we also show that this methodology is more amenable to being robustly scaled than the existing model selection algorithms used for this family. We demonstrate our techniques on simulated datasets.