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

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Ensemble samplers with affine invariance

Abstract

Keywords

ASJC Scopus subject areas

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