Uncertainty in Artificial Intelligence
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Graphical Models and Exponential Families
Dan Geiger, Christopher Meek
Abstract:
We provide a classification of graphical models according to their representation as subfamilies of exponential families. Undirected graphical models with no hidden variables are linear exponential families (LEFs), directed acyclic graphical models and chain graphs with no hidden variables, including Bayesian networks with several families of local distributions, are curved exponential families (CEFs) and graphical models with hidden variables are stratified exponential families (SEFs). An SEF is a finite union of CEFs satisfying a frontier condition. In addition, we illustrate how one can automatically generate independence and non-independence constraints on the distributions over the observable variables implied by a Bayesian network with hidden variables. The relevance of these results for model selection is examined.
Keywords:
Pages: 156-165
PS Link:
PDF Link: /papers/98/p156-geiger.pdf
BibTex:
@INPROCEEDINGS{Geiger98,
AUTHOR = "Dan Geiger and Christopher Meek",
TITLE = "Graphical Models and Exponential Families",
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 = "156--165"
}


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