Bayesian representations using chain event graphs
Anderson, Paul E. and Smith, J. Q. (2006) Bayesian representations using chain event graphs. Working Paper. Coventry: University of Warwick. Centre for Research in Statistical Methodology. Working papers, Vol.2006 (No.8).
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Official URL: http://www2.warwick.ac.uk/fac/sci/statistics/crism...
Bayesian networks (BNs) are useful for coding conditional independence statements between a given set of measurement variables.
On the other hand, event trees (ETs) are convenient for representing asymmetric structure and how situations unfold. In this paper
we report the development of a new graphical framework for discrete
probability models called the Chain Event Graph (CEG). The class of
CEG models contains finite BNs as a special case. Unlike the BN, the
CEG is equally appropriate for representing conditional independencies in asymmetric systems and does not need dependent variables to
be specified in advance. As with the BN, it also provides a framework
for learning relevant conditional probabilities and propagation. Furthermore, being a function of an ET, the CEG is a more
exible way of
representing various causal hypotheses than the BN. This new framework is illustrated throughout by a biological regulatory network: the
tryptophan metabolic pathway in the bacterium E. coli.
|Item Type:||Working or Discussion Paper (Working Paper)|
|Subjects:||Q Science > QA Mathematics|
|Divisions:||Faculty of Science > Statistics|
|Library of Congress Subject Headings (LCSH):||Graphical modeling (Statistics), Biological control systems -- Mathematical models, Escherichia coli -- Mathematical models, Bayesian statistical decision theory|
|Series Name:||Working papers|
|Publisher:||University of Warwick. Centre for Research in Statistical Methodology|
|Place of Publication:||Coventry|
|Number of Pages:||20|
|Status:||Not Peer Reviewed|
|Access rights to Published version:||Open Access|
|Funder:||Engineering and Physical Sciences Research Council (EPSRC), Biotechnology and Biological Sciences Research Council (Great Britain) (BBSRC), BioSim|
 C. Boutilier, N. Friedman, M. Goldszmidt, and D. Koller. Context-
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