Uncertainty in Artificial Intelligence
First Name   Last Name   Password   Forgot Password   Log in!
    Proceedings         Authors   Author's Info   Article details         Search    
Quantifier Elimination for Statistical Problems
Dan Geiger, Christopher Meek
Abstract:
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.
Keywords:
Pages: 226-235
PS Link:
PDF Link: /papers/99/p226-geiger.pdf
BibTex:
@INPROCEEDINGS{Geiger99,
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"
}


hosted by DSL   •   site info   •   help