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Abstract
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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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Keywords
Markov chain Monte Carlo, affine invariance, ensemble
samplers
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Mathematical Subject Classification 2000
Primary: 65C05
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Milestones
Received: 6 November 2009
Accepted: 29 November 2009
Published: 31 January 2010
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