Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling

TL;DR

This paper applies Bayesian model averaging to Chain Event Graphs to better quantify model uncertainty and improve robustness in probabilistic graphical modeling.

Contribution

It introduces a simple modification to existing algorithms enabling Bayesian model averaging over CEGs, addressing the limitations of MAP selection.

Findings

Bayesian model averaging captures model uncertainty effectively.

The approach yields more robust inference than MAP selection.

Sampling-based method is computationally feasible for larger problems.

Abstract

Chain Event Graphs (CEGs) are a widely applicable class of probabilistic graphical model that can represent context-specific independence statements and asymmetric unfoldings of events in an easily interpretable way. Existing model selection literature on CEGs has largely focused on obtaining the maximum a posteriori (MAP) CEG. However, MAP selection is well-known to ignore model uncertainty. Here, we explore the use of Bayesian model averaging over this class. We demonstrate how this approach can quantify model uncertainty and leads to more robust inference by identifying shared features across multiple high-scoring models. Because the space of possible CEGs is huge, scoring models exhaustively for model averaging in all but small problems is prohibitive. However, we provide a simple modification of an existing model selection algorithm, that samples the model space, to illustrate the efficacy of Bayesian model averaging compared to more standard MAP modelling.