Communications in Applied Mathematics and Computational Science

Ensemble samplers with affine invariance

Jonathan Goodman and Jonathan Weare

Full-text: Open access

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.

Article information

Source
Commun. Appl. Math. Comput. Sci., Volume 5, Number 1 (2010), 65-80.

Dates
Received: 6 November 2009
Accepted: 29 November 2009
First available in Project Euclid: 20 December 2017

Permanent link to this document
https://projecteuclid.org/euclid.camcos/1513731992

Digital Object Identifier
doi:10.2140/camcos.2010.5.65

Mathematical Reviews number (MathSciNet)
MR2600822

Zentralblatt MATH identifier
1189.65014

Subjects
Primary: 65C05: Monte Carlo methods

Keywords
Markov chain Monte Carlo affine invariance ensemble samplers

Citation

Goodman, Jonathan; Weare, Jonathan. Ensemble samplers with affine invariance. Commun. Appl. Math. Comput. Sci. 5 (2010), no. 1, 65--80. doi:10.2140/camcos.2010.5.65. https://projecteuclid.org/euclid.camcos/1513731992


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