Dynamic Bayesian Networks for Classification of Business Cycles
| 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.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.identifier.uri | http://hdl.handle.net/2003/4953 | |
| dc.identifier.uri | http://dx.doi.org/10.17877/DE290R-15091 | |
| 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 |