CHAIN EVENT GRAPH MAP MODEL SELECTION

Peter A. Thwaites, Guy Freeman, Jim Q. Smith

2009

Abstract

When looking for general structure from a finite discrete data set one can search over the class of Bayesian Networks (BNs). The class of Chain Event Graph (CEG) models is however much more expressive and is particularly suited to depicting hypotheses about how situations might unfold. Like the BN, the CEG admits conjugate learning on its conditional probability parameters using product Dirichlet priors. The Bayes Factors associated with different CEG models can therefore be calculated in an explicit closed form, which means that search for the maximum a posteriori (MAP) model in this class can be enacted by evaluating the score function of successive models and optimizing. Local search algorithms can be devised for the class of candidate models, but in this paper we concentrate on the process of scoring the members of this class.

Paper Citation

in Harvard Style

Thwaites P., Freeman G. and Smith J. (2009). CHAIN EVENT GRAPH MAP MODEL SELECTION . In Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2009) ISBN 978-989-674-012-2, pages 392-395. DOI: 10.5220/0002292403920395

in Bibtex Style

@conference{keod09,
author={Peter A. Thwaites and Guy Freeman and Jim Q. Smith},
title={CHAIN EVENT GRAPH MAP MODEL SELECTION},
booktitle={Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2009)},
year={2009},
pages={392-395},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002292403920395},
isbn={978-989-674-012-2},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Engineering and Ontology Development - Volume 1: KEOD, (IC3K 2009)
TI - CHAIN EVENT GRAPH MAP MODEL SELECTION
SN - 978-989-674-012-2
AU - Thwaites P.
AU - Freeman G.
AU - Smith J.
PY - 2009
SP - 392
EP - 395
DO - 10.5220/0002292403920395