In this paper we show that it can also be a powerful representational tool for a wide variety of causal hypotheses in such domains. Furthermore, we demonstrate ...

In this paper we show that it can also be a powerful representational tool for a wide variety of causal hypotheses in such domains. Furthermore, we demonstrate ...

Mar 28, 2024 · Both BNs and chain event graphs (CEGs) enjoy the flexibility of embedding probabilistic knowledge, managing probability propagation, inference, ...

This paper introduces a separation theorem for CEGs, analogous to the d-separation theorem for BNs, which likewise allows an analyst to identify the ...

Oct 22, 2024 · Causal chain graphs (CEGs, which are similar to ADMGs) are yet another class of graphs for which identifiability of interventions has been ...

Apr 23, 2024 · We apply a probabilistic graphical model called the chain event graph (CEG) to represent the failures and processes of deterioration of a system.

May 20, 2010 · As the Chain Event Graph (CEG) has a topology which represents sets of conditional independence statements, it becomes especially useful ...

Apr 23, 2024 · We apply a probabilistic graphical model called the chain event graph (CEG) to represent the failures and processes of deterioration of a system.

We prove that, as for a BN, identifiability analyses of causal effects can be performed through examining the topology of the CEG graph, leading to theorems ...

Here we apply the Chain Event Graph (CEG) which is a probabilistic graphical model derived from an underlying event tree. This class of model retains the ...