Ensemble samplers with affine invariance

作者：Jonathan B. Goodman, Jonathan Weare · 发表于：Communications in Applied Mathematics and Computational Science · 年份：2010 · DOI：10.2140/camcos.2010.5.65 · 被引用次数：3181 · 研究领域：Markov Chains and Monte Carlo Methods、Bayesian Methods and Mixture Models、Theoretical and Computational Physics

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.