Search Results for author:
Found 7 papers, 1 papers with code
Learning from data with structured missingness
Missing data are an unavoidable complication in many machine learning tasks.
Where the Bee Sucks -- A Dynamic Bayesian Network Approach to Decision Support for Pollinator Abundance Strategies
For policymakers wishing to make evidence-based decisions, one of the challenges is how to combine the relevant information and evidence in a coherent and defensible manner in order to formulate and evaluate candidate policies.
cegpy: Modelling with Chain Event Graphs in Python
However, there currently exists no software that allows a user to leverage the theoretical developments of the CEG model family in modelling processes with structural asymmetries.
Beyond Conjugacy for Chain Event Graph Model Selection
Moreover, we also show that this methodology is more amenable to being robustly scaled than the existing model selection algorithms used for this family.
A Bayesian decision support system for counteracting activities of terrorist groups
Activities of terrorist groups present a serious threat to the security and well-being of the general public.
Constructing a Chain Event Graph from a Staged Tree
This coloured event tree, also known as a staged tree, is the output of the learning algorithms used for this family.
Propagation for Dynamic Continuous Time Chain Event Graphs
Chain Event Graphs (CEGs) are a family of event-based graphical models that represent context-specific conditional independences typically exhibited by asymmetric state space problems.
