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Bayesian MAP model selection of chain event graphs

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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).

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Official URL: http://www2.warwick.ac.uk/fac/sci/statistics/crism...

Abstract

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

Official Date:

2009

Dates:

Date	Event
2009	Published

Volume:

Vol.2009

Number:

No.6

Number of Pages:

Status:

Not Peer Reviewed

Access rights to Published version:

Open Access

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