Written by some major contributors to the development of this class of graphical models,
Chain Event Graphs introduces a viable and straightforward new tool for statistical inference …

When facing extremely large and interconnected systems, decision-makers must often
combine evidence obtained from multiple expert domains, each informed by a distinct panel …

We study the stability of posterior predictive inferences to the specification of the likelihood
model and perturbations of the data generating process. In modern big data analyses, useful …

Recent results concerning the instability of Bayes Factor search over Bayesian Networks
(BN's) lead us to ask whether learning the parameters of a selected BN might also depend …

A new tree-based graphical model---the dynamic staged tree---is proposed for modelling
discrete-valued discrete-time multivariate processes which are hypothesised to exhibit …

In this paper we investigate the geometry of a discrete Bayesian network whose graph is a
tree all of whose variables are binary and the only observed variables are those labeling its …

The local sensitivity analysis is recognized for its computational simplicity, and potential use
in multi-dimensional and complex problems. Unfortunately, its major drawback is its …

Now that Bayesian Networks (BNs) have become widely used, an appreciation is
developing of just how critical an awareness of the sensitivity and robustness of certain …

This thesis explores the robustness of large discrete Bayesian networks (BNs) when applied
in decision support systems which have a pre-specified subset of target variables. We …

A new tree-based graphical model—the dynamic staged tree—is used to model discrete-
valued discrete-time multivariate processes which are hypothesised to exhibit certain …