Uncertainty in Artificial Intelligence
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On the Geometry of Bayesian Graphical Models with Hidden Variables
Raffaella Settimi, Jim Smith
Abstract:
In this paper we investigate the geometry of the likelihood of the unknown parameters in a simple class of Bayesian directed graphs with hidden variables. This enables us, before any numerical algorithms are employed, to obtain certain insights in the nature of the unidentifiability inherent in such models, the way posterior densities will be sensitive to prior densities and the typical geometrical form these posterior densities might take. Many of these insights carry over into more complicated Bayesian networks with systematic missing data.
Keywords: Bayesian learning, Bayesian networks, identifiability, latent structure analysis.
Pages: 472-479
PS Link:
PDF Link: /papers/98/p472-settimi.pdf
BibTex:
@INPROCEEDINGS{Settimi98,
AUTHOR = "Raffaella Settimi and Jim Smith",
TITLE = "On the Geometry of Bayesian Graphical Models with Hidden Variables",
BOOKTITLE = "Proceedings of the Fourteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-98)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1998",
PAGES = "472--479"
}


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