Ensemble samplers with affine invariance

Jonathan Goodman; Jonathan Weare

doi:10.2140/camcos.2010.5.65

Ensemble samplers with affine invariance

Jonathan Goodman, Jonathan Weare

Mathematics

Research output: Contribution to journal › Article › peer-review

Abstract

We propose a family of Markov chain Monte Carlo methods whose performance is unaffected by affine tranformations of space. These algorithms are easy to construct and require little or no additional computational overhead. They should be particularly useful for sampling badly scaled distributions. Computational tests show that the affine invariant methods can be significantly faster than standard MCMC methods on highly skewed distributions.

Original language	English (US)
Pages (from-to)	65-80
Number of pages	16
Journal	Communications in Applied Mathematics and Computational Science
Volume	5
Issue number	1
DOIs	https://doi.org/10.2140/camcos.2010.5.65
State	Published - 2010

Keywords

Affine invariance
Ensemble samplers
Markov chain Monte Carlo

ASJC Scopus subject areas

Computer Science Applications
Computational Theory and Mathematics
Applied Mathematics

Access to Document

10.2140/camcos.2010.5.65

Fingerprint Dive into the research topics of 'Ensemble samplers with affine invariance'. Together they form a unique fingerprint.

Cite this

@article{64ebe435829647f594fea6d945067389,

title = "Ensemble samplers with affine invariance",

abstract = "We propose a family of Markov chain Monte Carlo methods whose performance is unaffected by affine tranformations of space. These algorithms are easy to construct and require little or no additional computational overhead. They should be particularly useful for sampling badly scaled distributions. Computational tests show that the affine invariant methods can be significantly faster than standard MCMC methods on highly skewed distributions.",

keywords = "Affine invariance, Ensemble samplers, Markov chain Monte Carlo",

author = "Jonathan Goodman and Jonathan Weare",

year = "2010",

doi = "10.2140/camcos.2010.5.65",

language = "English (US)",

volume = "5",

pages = "65--80",

journal = "Communications in Applied Mathematics and Computational Science",

issn = "1559-3940",

publisher = "Mathematical Sciences Publishers",

number = "1",

}

TY - JOUR

T1 - Ensemble samplers with affine invariance

AU - Goodman, Jonathan

AU - Weare, Jonathan

PY - 2010

Y1 - 2010

N2 - We propose a family of Markov chain Monte Carlo methods whose performance is unaffected by affine tranformations of space. These algorithms are easy to construct and require little or no additional computational overhead. They should be particularly useful for sampling badly scaled distributions. Computational tests show that the affine invariant methods can be significantly faster than standard MCMC methods on highly skewed distributions.

AB - We propose a family of Markov chain Monte Carlo methods whose performance is unaffected by affine tranformations of space. These algorithms are easy to construct and require little or no additional computational overhead. They should be particularly useful for sampling badly scaled distributions. Computational tests show that the affine invariant methods can be significantly faster than standard MCMC methods on highly skewed distributions.

KW - Affine invariance

KW - Ensemble samplers

KW - Markov chain Monte Carlo

UR - http://www.scopus.com/inward/record.url?scp=85017381725&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85017381725&partnerID=8YFLogxK

U2 - 10.2140/camcos.2010.5.65

DO - 10.2140/camcos.2010.5.65

M3 - Article

AN - SCOPUS:85017381725

VL - 5

SP - 65

EP - 80

JO - Communications in Applied Mathematics and Computational Science

JF - Communications in Applied Mathematics and Computational Science

SN - 1559-3940

IS - 1

ER -