Uncertainty in Artificial Intelligence
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Chain Graphs for Learning
Wray Buntine
Abstract:
Chain graphs combine directed and undirected graphs and their underlying mathematics combines properties of the two. This paper gives a simplified definition of chain graphs based on a hierarchical combination of Bayesian (directed) and Markov (undirected) networks. Examples of a chain graph are multivariate feed-forward networks, clustering with conditional interaction between variables, and forms of Bayes classifiers. Chain graphs are then extended using the notation of plates so that samples and data analysis problems can be represented in a graphical model as well. Implications for learning are discussed in the conclusion.
Keywords: Learning, chain graphs, mixed directed and undirected networks.
Pages: 46-54
PS Link: http://www.ultimode.com/~wray/uai95.ps.Z
PDF Link: /papers/95/p46-buntine.pdf
BibTex:
@INPROCEEDINGS{Buntine95,
AUTHOR = "Wray Buntine ",
TITLE = "Chain Graphs for Learning",
BOOKTITLE = "Proceedings of the Eleventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-95)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1995",
PAGES = "46--54"
}


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