2008-07-09-thwaites08a.md

abstract	title	year	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	note	address	container-title	volume	genre	issued	pdf	extras
A Chain Event Graph (CEG) is a graphical model which is designed to embody conditional independencies in problems whose state spaces are highly asymmetric and do not admit a natural product structure. In this paper we present a probability propagation algorithm which uses the topology of the CEG to build a transporter CEG. Intriguingly, 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.	Propagation using Chain Event Graphs	2008	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	thwaites08a	0	Propagation using Chain Event Graphs	546	553	546-553	546	false	Thwaites, Peter A. and Smith, Jim Q. and Cowell, Robert G.	given family Peter A. Thwaites given family Jim Q. Smith given family Robert G. Cowell	2008-07-09	Reissued by PMLR on 09 October 2024.		Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence	R6	inproceedings	date-parts 2008 7 9	https://raw.githubusercontent.com/mlresearch/r6/main/assets/thwaites08a/thwaites08a.pdf