7 citations · 11 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
Beyond Conjugacy for Chain Event Graph Model Selection
Aditi Shenvi, Silvia Liverani
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 n…
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…
A Bayesian Dynamic Graphical Model for Recurrent Events in Public Health
Aditi Shenvi, Jim Q. Smith
To analyze the impacts of certain types of public health interventions we need to estimate the treatment effects and outcomes as these apply to heterogeneous open populations. Dyna…