Uncertainty in Artificial Intelligence
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On Testing Whether an Embedded Bayesian Network Represents a Probability Model
Dan Geiger, Azaria Paz, Judea Pearl
Abstract:
Testing the validity of probabilistic models containing unmeasured (hidden) variables is shown to be a hard task. We show that the task of testing whether models are structurally incompatible with the data at hand, requires an exponential number of independence evaluations, each of the form: "X is conditionally independent of Y, given Z." In contrast, a linear number of such evaluations is required to test a standard Bayesian network (one per vertex). On the positive side, we show that if a network with hidden variables G has a tree skeleton, checking whether G represents a given probability model P requires the polynomial number of such independence evaluations. Moreover, we provide an algorithm that efficiently constructs a tree-structured Bayesian network (with hidden variables) that represents P if such a network exists, and further recognizes when such a network does not exist.
Keywords:
Pages: 244-252
PS Link:
PDF Link: /papers/94/p244-geiger.pdf
BibTex:
@INPROCEEDINGS{Geiger94,
AUTHOR = "Dan Geiger and Azaria Paz and Judea Pearl",
TITLE = "On Testing Whether an Embedded Bayesian Network Represents a Probability Model",
BOOKTITLE = "Proceedings of the Tenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-94)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1994",
PAGES = "244--252"
}


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