Bayesian MAP model selection of chain event graphs
Freeman, Guy and Smith, J. Q. (2009) Bayesian MAP model selection of chain event graphs. Working Paper. Coventry: University of Warwick. Centre for Research in Statistical Methodology. Working papers, Vol.2009 (No.6).
WRAP_Freeman_09-06w.pdf - Published Version - Requires a PDF viewer such as GSview, Xpdf or Adobe Acrobat Reader
Official URL: http://www2.warwick.ac.uk/fac/sci/statistics/crism...
The class of chain event graph models is a generalisation of the class of discrete Bayesian networks,
retaining most of the structural advantages of the Bayesian network for model interrogation,
propagation and learning, while more naturally encoding asymmetric state spaces and the order in
which events happen. In this paper we demonstrate how with complete sampling, conjugate closed
form model selection based on product Dirichlet priors is possible, and prove that suitable homogeneity
assumptions characterise the product Dirichlet prior on this class of models. We demonstrate
our techniques using two educational examples.
|Item Type:||Working or Discussion Paper (Working Paper)|
|Subjects:||Q Science > QA Mathematics|
|Divisions:||Faculty of Science > Statistics|
|Library of Congress Subject Headings (LCSH):||Distribution (Probability theory), Bayesian statistical decision theory, Graphical modeling (Statistics)|
|Series Name:||Working papers|
|Publisher:||University of Warwick. Centre for Research in Statistical Methodology|
|Place of Publication:||Coventry|
|Number of Pages:||19|
|Status:||Not Peer Reviewed|
|Access rights to Published version:||Open Access|
 R. G. Cowell, A. P. Dawid, S. L. Lauritzen, D. J. Spiegelhalter, Probabilistic Networks and
Actions (login required)
Downloads per month over past year