Finding Non-Overlapping Clusters for Generalized Inference Over Graphical Models
Volume: 60, Issue: 12, Pages: 6368 - 6381
Published: Dec 1, 2012
Abstract
Graphical models use graphs to compactly capture stochastic dependencies amongst a collection of random variables. Inference over graphical models corresponds to finding marginal probability distributions given joint probability distributions. In general, this is computationally intractable, which has led to a quest for finding efficient approximate inference algorithms. We propose a framework for generalized inference over graphical models that...
Paper Details
Title
Finding Non-Overlapping Clusters for Generalized Inference Over Graphical Models
Published Date
Dec 1, 2012
Volume
60
Issue
12
Pages
6368 - 6381
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