Skip navigation
Skip navigation

Projecting Ising Model Parameters for Fast Mixing

Domke, Justin; Liu, Xianghang


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
Source: Structured Learning via Logistic Regression


File Description SizeFormat Image
01_Domke_Projecting_Ising_Model_2013.pdf368.85 kBAdobe PDF    Request a copy

Items in Open Research are protected by copyright, with all rights reserved, unless otherwise indicated.

Updated:  19 May 2020/ Responsible Officer:  University Librarian/ Page Contact:  Library Systems & Web Coordinator