Irreversible Samplers from Jump and Continuous Markov Processes

In this paper, we propose irreversible versions of the Metropolis Hastings (MH) and Metropolis adjusted Langevin algorithm (MALA) with a main focus on the latter. For the former, we show how one can simply switch between different proposal and acceptance distributions upon rejection to obtain an irr...

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Bibliographic Details
Published inarXiv.org
Main Authors Yi-An, Ma, Fox, Emily B, Chen, Tianqi, Wu, Lei
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 12.03.2018
Subjects
Algorithms
Markov processes
Samplers
Statistics - Methodology
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Summary:In this paper, we propose irreversible versions of the Metropolis Hastings (MH) and Metropolis adjusted Langevin algorithm (MALA) with a main focus on the latter. For the former, we show how one can simply switch between different proposal and acceptance distributions upon rejection to obtain an irreversible jump sampler (I-Jump). The resulting algorithm has a simple implementation akin to MH, but with the demonstrated benefits of irreversibility. We then show how the previously proposed MALA method can also be extended to exploit irreversible stochastic dynamics as proposal distributions in the I-Jump sampler. Our experiments explore how irreversibility can increase the efficiency of the samplers in different situations.
ISSN:2331-8422
DOI:10.48550/arxiv.1608.05973