Uncertainty in Artificial Intelligence
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Quantifier Elimination for Statistical Problems
Dan Geiger, Christopher Meek
Recent improvement on Tarski's procedure for quantifier elimination in the first order theory of real numbers makes it feasible to solve small instances of the following problems completely automatically: 1. listing all equality and inequality constraints implied by a graphical model with hidden variables. 2. Comparing graphyical models with hidden variables (i.e., model equivalence, inclusion, and overlap). 3. Answering questions about the identification of a model or portion of a model, and about bounds on quantities derived from a model. 4. Determing whether a given set of independence assertions. We discuss the foundation of quantifier elimination and demonstrate its application to these problems.
Pages: 226-235
PS Link:
PDF Link: /papers/99/p226-geiger.pdf
AUTHOR = "Dan Geiger and Christopher Meek",
TITLE = "Quantifier Elimination for Statistical Problems",
BOOKTITLE = "Proceedings of the Fifteenth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-99)",
PUBLISHER = "Morgan Kaufmann",
ADDRESS = "San Francisco, CA",
YEAR = "1999",
PAGES = "226--235"

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