Dynamic Bayesian networks for classification of business cycles

We use Dynamic Bayesian networks to classify business cycle phases. We compare classifiers 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.

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Year of publication:	1999
Authors:	Sondhauss, Ursula ; Weihs, Claus
Publisher:	Dortmund : Universität Dortmund, Sonderforschungsbereich 475 - Komplexitätsreduktion in Multivariaten Datenstrukturen
Subject:	Konjunktur \| Bayes-Statistik \| Künstliche Intelligenz \| Theorie

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Series:	Technical Report ; 1999,17
Type of publication:	Book / Working Paper
Type of publication (narrower categories):	Working Paper
Language:	English
Other identifiers:	76804667X [GVK] hdl:10419/77317 [Handle] RePEc:zbw:sfb475:199917 [RePEc]
Source:	EconStor

Persistent link: https://www.econbiz.de/10010316660