Ensemble samplers with affine invariance
Jonathan GoodmanCourant Institute, New York University, 251 Mercer St., New York, NY 10012, United StatesJonathan WeareCourant Institute, New York University, 251 Mercer St., New York, NY 10012, United States
2010en
ABI
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.
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Cited by 10 references