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Irreversible Samplers from Jump and Continuous Markov Processes

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arxiv 1608.05973 v5 pith:VETIEBWH submitted 2016-08-21 stat.ME

Yi-An Ma , Emily B. Fox , Tianqi Chen , Lei Wu This is my paper · ORCID
classification stat.ME
keywords irreversiblealgorithmdifferentdistributionsi-jumpirreversibilityjumpmala
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Beyond Self-Repellent Kernels: History-Driven Target Towards Efficient Nonlinear MCMC on General Graphs

    cs.LG 2025-05 conditional novelty 7.0 of 10

    Replacing the target μ by a history-adjusted target μ(x/μ)^{-α} in any graph MCMC sampler gives O(1/α) variance reduction at constant per-step cost, and extends to non-reversible chains.

  2. True Self-Avoiding Walk for Accelerating Markov-Chain Monte Carlo Integration

    stat.CO 2026-05 unverdicted novelty 5.0 of 10

    TSAW-modified MCMC on finite irreducible chains achieves almost sure integral estimation error O(sqrt(log t)/t), improving on standard t^{-1/2} rates.

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