Quantifier Elimination for Statistical Problems
Abstract
Recent improvements 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 graphical 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. Determining whether an independence assertion is implied from a given set of independence assertions. We discuss the foundations of quantifier elimination and demonstrate its application to these problems.
Cite
Text
Geiger and Meek. "Quantifier Elimination for Statistical Problems." Conference on Uncertainty in Artificial Intelligence, 1999.Markdown
[Geiger and Meek. "Quantifier Elimination for Statistical Problems." Conference on Uncertainty in Artificial Intelligence, 1999.](https://mlanthology.org/uai/1999/geiger1999uai-quantifier/)BibTeX
@inproceedings{geiger1999uai-quantifier,
title = {{Quantifier Elimination for Statistical Problems}},
author = {Geiger, Dan and Meek, Christopher},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {1999},
pages = {226-235},
url = {https://mlanthology.org/uai/1999/geiger1999uai-quantifier/}
}