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Projecting Ising Model Parameters for Fast Mixing

Domke, Justin; Liu, Xianghang

Description

Inference in general Ising models is difficult, due to high treewidth making treebased algorithms intractable. Moreover, when interactions are strong, Gibbs sampling may take exponential time to converge to the stationary distribution. We present an algorithm to project Ising model parameters onto a parameter set that is guaranteed to be fast mixing, under several divergences. We find that Gibbs sampling using the projected parameters is more accurate than with the original parameters when...[Show more]

CollectionsANU Research Publications
Date published: 2013
Type: Conference paper
URI: http://hdl.handle.net/1885/66092
Source: Structured Learning via Logistic Regression

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