## Dynamic Bayesian Networks for Classification of Business Cycles (1999)

Citations: | 3 - 0 self |

### BibTeX

@TECHREPORT{Sondhauss99dynamicbayesian,

author = {Ursula Sondhauss and Claus Weihs},

title = {Dynamic Bayesian Networks for Classification of Business Cycles},

institution = {},

year = {1999}

}

### OpenURL

### Abstract

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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Citation Context ...GNP) alone and an analysis of the influence of these variables. Probabilistic inference in Bayesian networks can be used to answer diagnostic questions of influence and characterization of that kind (=-=Pearl, 1988-=-). It has also been shown that Bayesian networks can be used successfully for classification (Friedman, Geiger, and Goldszmidt, FGG, 1997). Dynamic Bayesian networks (Dean and Kanazawa 1988) can model... |

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