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Parameter Learning for Hybrid Bayesian Networks With Gaussian Mixture and Dirac Mixture Conditional Densities

Krauthausen, Peter; Hanebeck, Uwe D.

In this paper, the first algorithm for learning hybrid Bayesian Networks with Gaussian mixture and Dirac mixture conditional densities from data given their structure is presented. The mixture densities to be learned allow for nonlinear dependencies between the variables and exact closedform inference. For learning the network's parameters, an incremental gradient ascent algorithm is derived. Analytic expressions for the partial derivatives and their combination with messages are presented. This hybrid approach subsumes the existing approach for purely discrete-valued networks and is applicable to partially observable networks, too. Its practicability is demonstrated by a reference example.

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Volltext §
DOI: 10.5445/IR/1000035096
Zugehörige Institution(en) am KIT Institut für Anthropomatik (IFA)
Publikationstyp Proceedingsbeitrag
Jahr 2010
Sprache Englisch
Identifikator ISBN: 978-1-4244-7427-1
KITopen-ID: 1000035096
Erschienen in Proceedings of the 2010 American Control Conference (ACC 2010), Baltimore, Maryland, USA, June 30-July 02, 2010
Verlag IEEE, Piscataway
Seiten 480-485
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