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Quantifier Elimination for Statistical Problems

2013/01/23 by Dan Geiger, Christopher Meek, Geiger, Dan +1
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Logic in Computer Science (cs.LO) #cs.AI #cs.LO

paper · pdf · doi:10.48550/arxiv.1301.6698

Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999)

arxiv created 2013/01/23 · arxiv updated 2013/01/30

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.

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