Dynamic Bayesian Networks for Classification of Business Cycles

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DC Field	Value	Language
dc.contributor.author	Sondhauss, Ursula	de
dc.contributor.author	Weihs, Claus	de
dc.date.accessioned	2004-12-06T18:40:24Z	-
dc.date.available	2004-12-06T18:40:24Z	-
dc.date.issued	1999	de
dc.identifier.uri	http://hdl.handle.net/2003/4953	-
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-15091	-
dc.description.abstract	We use Dynamic Bayesian networks to classify business cycle phases. We compare classiffiers generated by learning the Dynamic Bayesian network structure on different sets of admissible network structures. Included are sets of network structures of the Tree Augmented Naive Bayes (TAN) classifiers of Friedman, Geiger, and Goldszmidt (1997) adapted for dynamic domains. The performance of the developed classifiers on the given data was modest.	en
dc.format.extent	299673 bytes	-
dc.format.extent	972962 bytes	-
dc.format.mimetype	application/pdf	-
dc.format.mimetype	application/postscript	-
dc.language.iso	en	de
dc.publisher	Universitätsbibliothek Dortmund	de
dc.subject.ddc	310	de
dc.title	Dynamic Bayesian Networks for Classification of Business Cycles	en
dc.type	Text	de
dc.type.publicationtype	report	en
dcterms.accessRights	open access	-
Appears in Collections:	Sonderforschungsbereich (SFB) 475

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