1 citations · 2 across the 6 of their papers we have counts for
6 papers
cegpy: Modelling with Chain Event Graphs in Python
Gareth Walley, Aditi Shenvi, Peter Strong +1
Chain event graphs (CEGs) are a recent family of probabilistic graphical models that generalise the popular Bayesian networks (BNs) family. Crucially, unlike BNs, a CEG is able to…
Scalable Model Selection for Staged Trees: Mean-posterior Clustering and Binary Trees
Peter Strong, Jim Q. Smith
Several structure-learning algorithms for staged trees, asymmetric extensions of Bayesian networks, have been proposed. However, these either do not scale efficiently as the number…
Score Equivalence for Staged Trees
Conor Hughes, Peter Strong, Aditi Shenvi
Staged trees are a recently-developed, powerful family of probabilistic graphical models. An equivalence class of staged trees has now been characterised, and two fundamental stati…
Bayesian Model Averaging of Chain Event Graphs for Robust Explanatory Modelling
Peter Strong, Jim Q Smith
Chain Event Graphs (CEGs) are a widely applicable class of probabilistic graphical model that can represent context-specific independence statements and asymmetric unfoldings of ev…
A Bayesian Analysis of Migration Pathways using Chain Event Graphs of Agent Based Models
Peter Strong, Alys McAlpine, Jim Q Smith
Agent-Based Models (ABMs) are often used to model migration and are increasingly used to simulate individual migrant decision-making and unfolding events through a sequence of heur…
Building A Bayesian Decision Support System for Evaluating COVID-19 Countermeasure Strategies
Peter Strong, Aditi Shenvi, Xuewen Yu +3
Decision making in the face of a disaster requires the consideration of several complex factors. In such cases, Bayesian multi-criteria decision analysis provides a framework for d…