Projecting Ising model parameters for fast mixing

Justin Domke; Xianghang Liu

Projecting Ising model parameters for fast mixing

Justin Domke, Xianghang Liu

Research output: Contribution to journal › Conference article › peer-review

5 Citations (Scopus)

Abstract

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 interaction strengths are strong and when limited time is available for sampling.

Original language	English
Journal	Advances in Neural Information Processing Systems
Publication status	Published - 2013
Event	27th Annual Conference on Neural Information Processing Systems, NIPS 2013 - Lake Tahoe, NV, United States Duration: 5 Dec 2013 → 10 Dec 2013

Cite this

@article{f942b20651ae48e59ac665ecc60303b7,

title = "Projecting Ising model parameters for fast mixing",

abstract = "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 interaction strengths are strong and when limited time is available for sampling.",

author = "Justin Domke and Xianghang Liu",

year = "2013",

language = "English",

journal = "Advances in Neural Information Processing Systems",

issn = "1049-5258",

publisher = "Neural information processing systems foundation",

note = "27th Annual Conference on Neural Information Processing Systems, NIPS 2013 ; Conference date: 05-12-2013 Through 10-12-2013",

}

TY - JOUR

T1 - Projecting Ising model parameters for fast mixing

AU - Domke, Justin

AU - Liu, Xianghang

PY - 2013

Y1 - 2013

N2 - 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 interaction strengths are strong and when limited time is available for sampling.

AB - 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 interaction strengths are strong and when limited time is available for sampling.

UR - http://www.scopus.com/inward/record.url?scp=84898939558&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:84898939558

SN - 1049-5258

JO - Advances in Neural Information Processing Systems

JF - Advances in Neural Information Processing Systems

T2 - 27th Annual Conference on Neural Information Processing Systems, NIPS 2013

Y2 - 5 December 2013 through 10 December 2013

ER -

Projecting Ising model parameters for fast mixing

Abstract

Other files and links

Fingerprint

Cite this