Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling

Peter Strong, Jim Q. Smith
Proceedings of The 11th International Conference on Probabilistic Graphical Models, PMLR 186:61-72, 2022.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v186-strong22a, title = {Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling}, author = {Strong, Peter and Smith, Jim Q.}, booktitle = {Proceedings of The 11th International Conference on Probabilistic Graphical Models}, pages = {61--72}, year = {2022}, editor = {Salmerón, Antonio and Rumı́, Rafael}, volume = {186}, series = {Proceedings of Machine Learning Research}, month = {05--07 Oct}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v186/strong22a/strong22a.pdf}, url = {https://proceedings.mlr.press/v186/strong22a.html}, 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.} }
Endnote
%0 Conference Paper %T Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling %A Peter Strong %A Jim Q. Smith %B Proceedings of The 11th International Conference on Probabilistic Graphical Models %C Proceedings of Machine Learning Research %D 2022 %E Antonio Salmerón %E Rafael Rumı́ %F pmlr-v186-strong22a %I PMLR %P 61--72 %U https://proceedings.mlr.press/v186/strong22a.html %V 186 %X 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.
APA
Strong, P. & Smith, J.Q.. (2022). Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling. Proceedings of The 11th International Conference on Probabilistic Graphical Models, in Proceedings of Machine Learning Research 186:61-72 Available from https://proceedings.mlr.press/v186/strong22a.html.

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