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) |
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Pages (from-to) | 65-80 |
Number of pages | 16 |
Journal | Communications in Applied Mathematics and Computational Science |
Volume | 5 |
Issue number | 1 |
DOIs | |
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