Uncertainty in Artificial Intelligence
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A Sequence of Relaxation Constraining Hidden Variable Models
Greg Ver Steeg, Aram Galstyan
Many widely studied graphical models with latent variables lead to nontrivial constraints on the distribution of the observed variables. Inspired by the Bell inequalities in quantum mechanics, we refer to any linear inequality whose violation rules out some latent variable model as a "hidden variable test" for that model. Our main contribution is to introduce a sequence of relaxations which provides progressively tighter hidden variable tests. We demonstrate applicability to mixtures of sequences of i.i.d. variables, Bell inequalities, and homophily models in social networks. For the last, we demonstrate that our method provides a test that is able to rule out latent homophily as the sole explanation for correlations on a real social network that are known to be due to influence.
Pages: 717-726
PS Link:
PDF Link: /papers/11/p717-ver_steeg.pdf
AUTHOR = "Greg Ver Steeg and Aram Galstyan",
TITLE = "A Sequence of Relaxation Constraining Hidden Variable Models",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "717--726"

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