Propagation using Chain Event Graphs

Abstract

A Chain Event Graph (CEG) is a graphial model which designed to embody conditional independencies in problems whose state spaces are highly asymmetric and do not admit a natural product structure. In this paer we present a probability propagation algorithm which uses the topology of the CEG to build a transporter CEG. Intriungly,the transporter CEG is directly analogous to the triangulated Bayesian Network (BN) in the more conventional junction tree propagation algorithms used with BNs. The propagation method uses factorization formulae also analogous to (but different from) the ones using potentials on cliques and separators of the BN. It appears that the methods will be typically more efficient than the BN algorithms when applied to contexts where there is significant asymmetry present.

Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI2008)

References in corpus (1)

• Context-Specific Independence in Bayesian Networks

Cited by in corpus (6)

• A Separation Theorem for Chain Event Graphs
• Sensitivity analysis, multilinearity and beyond
• An N Time-Slice Dynamic Chain Event Graph
• Propagation for Dynamic Continuous Time Chain Event Graphs
• Some context-specific graphical models for discrete longitudinal data
• Discovery of statistical equivalence classes using computer algebra