The Chain Event Graph

Collazo, Rodrigo A.; Görgen, Christiane; Smith, Jim Q.

doi:10.1201/9781315120515-3

Cited by 7 publications

(12 citation statements)

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“…The AHC algorithm in [9] sequentially merges situations into stages by combining the two situations at every step which give the highest improvement to the BDeu score of the graph. This was further refined by the introduction of the concept of hyperstages in [3]. A hyperstage consists of sets of situations such that two situations can be merged into a single stage if and only if they belong to the same set in the hyperstage.…”

Section: Methodsmentioning

confidence: 99%

Modelling with Non-stratified Chain Event Graphs

Shenvi

Smith

Eldridge

2019

Springer Proceedings in Mathematics &Amp; Statistics

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Chain Event Graphs (CEGs) are a recent probabilistic graphical modelling tool that have proved successful in modelling scenarios with context-specific independencies. Although the theory underlying CEGs supports appropriate representation of structural zeroes, the literature so far does not provide an adaptation of the vanilla CEG methods for a real-world application presenting structural zeroes also known as the non-stratified CEG class. To illustrate these methods, we present a non-stratified CEG representing a public health intervention designed to reduce the risk and rate of falling in the elderly. We then compare the CEG model to the more conventional Bayesian Network model when applied to this setting.

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Section: Methodsmentioning

confidence: 99%

Modelling with Non-stratified Chain Event Graphs

Shenvi

Smith

Eldridge

2019

Springer Proceedings in Mathematics &Amp; Statistics

Self Cite

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“…To ensure all evaluated models are interpretable, we must use the concept of a hyperstage [Collazo, 2017]. A hyperstage, H H H, is a collection of sets containing the situations of an event tree T such that any two situations s and s can be in the same stage only if there is a set H i ∈ H H H such that s, s ∈ H i .…”

Section: Hyper-stage Settingmentioning

confidence: 99%

Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling

Strong¹,

Smith²

2022

Preprint

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Chain Event Graphs (CEGs) are a widely applicable class of probabilistic graphical models that are able to represent context-specific independence statements and asymmetric unfoldings of events in an easily interpretable way. Existing model selection literature on CEGs has 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 model averaging over this class. We demonstrate that such methods express model uncertainty and lead to more robust inference. Because the space of possible CEGs is huge, scoring models exhaustively for model averaging in all but small problems is prohibitive. However we show that a bespoke class of hybrid forward sampling and greedy search algorithms can successfully and intelligently traverse this space of candidate models. By applying a simple version of our search method to two known case studies, we can illustrate the efficacy of such methods compared to more standard MAP modelling. We also demonstrate how its outputs systematically inform those component hypotheses that are most robustly supported the data and high-scoring alternative models to the MAP model.

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“…Lastly, we provide Python code that obtains a staged tree using an Agglomerative Hierarchical Clustering (AHC) algorithm and then transforms it into a CEG using our algorithm. Unlike the existing 'ceg' R package (Collazo and Taranti, 2017), our code is not restricted to SCEGs and it also allows manual addition of edges with sampling zeros.…”

Section: Introductionmentioning

confidence: 99%

Constructing a Chain Event Graph from a Staged Tree

Shenvi¹,

Smith²

2020

Preprint

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Chain Event Graphs (CEGs) are a recent family of probabilistic graphical models -a generalisation of Bayesian Networks -providing an explicit representation of structural zeros and context-specific conditional independences within their graph topology. A CEG is constructed from an event tree through a sequence of transformations beginning with the colouring of the vertices of the event tree to identify one-step transition symmetries. This coloured event tree, also known as a staged tree, is the output of the learning algorithms used for this family. Surprisingly, no general algorithm has yet been devised that automatically transforms any staged tree into a CEG representation. In this paper we provide a simple iterative backward algorithm for this transformation. Additionally, we show that no information is lost from transforming a staged tree into a CEG. Finally, we demonstrate that with an optimal stopping time, our algorithm is more efficient than the generalisation of a special case presented in Silander and Leong (2013). We also provide Python code using this algorithm to obtain a CEG from any staged tree along with the functionality to add edges with sampling zeros.

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The Chain Event Graph

Cited by 7 publications

References 0 publications

Modelling with Non-stratified Chain Event Graphs

Modelling with Non-stratified Chain Event Graphs

Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling

Constructing a Chain Event Graph from a Staged Tree

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