Improving the asymptotic performance of Markov chain Monte-Carlo by inserting vortices

Yi Sun, Faustino Gomez, Jürgen Schmidhuber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations


We present a new way of converting a reversible finite Markov chain into a nonreversible one, with a theoretical guarantee that the asymptotic variance of the MCMC estimator based on the non-reversible chain is reduced. The method is applicable to any reversible chain whose states are not connected through a tree, and can be interpreted graphically as inserting vortices into the state transition graph. Our result confirms that non-reversible chains are fundamentally better than reversible ones in terms of asymptotic performance, and suggests interesting directions for further improving MCMC.
Original languageEnglish (US)
Title of host publicationAdvances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010
StatePublished - Dec 1 2010
Externally publishedYes

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