Authors: Sondhauss, Ursula
Weihs, Claus
Title: Dynamic Bayesian Networks for Classification of Business Cycles
Language (ISO): en
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.
URI: http://hdl.handle.net/2003/4953
http://dx.doi.org/10.17877/DE290R-15091
Issue Date: 1999
Publisher: Universitätsbibliothek Dortmund
Appears in Collections:Sonderforschungsbereich (SFB) 475

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