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New research offers faster Markov chain convergence methods

By PulseAugur Editorial · [2 sources] ·

Two new research papers propose novel methods for accelerating Markov chain convergence. The first paper introduces a criterion called asymptotic equivalence with the target, offering a direct route to convergence proofs without relying on traditional concepts like irreducibility or aperiodicity. The second paper presents a framework called Delayed Acceptance with Regularisation and Tempering (DART), which achieves faster mixing times for Markov chains by exploiting geometric information of the target density without direct gradient calculations. AI

IMPACT These methods could improve the efficiency of sampling algorithms used in machine learning and statistical inference.

RANK_REASON Two academic papers published on arXiv present novel theoretical contributions to Markov chain convergence.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New research offers faster Markov chain convergence methods

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Two academic papers published on arXiv present novel theoretical contributions to Markov chain convergence.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Patrick Forr\'e ·

    A Direct Route to Markov Chain Convergence via Asymptotic Equivalence with the Target

    arXiv:2608.03353v1 Announce Type: cross Abstract: For a Markov kernel $T$ with an invariant probability measure $\pi$, we give a self-contained proof of the Markov chain convergence theorem via a criterion called asymptotic equivalence with the target. It assumes two parts about …

  2. arXiv stat.ML TIER_1 English(EN) · Robert Kutri, Robert Scheichl ·

    Fast-Mixing Markov Chains without Gradients

    arXiv:2606.27564v1 Announce Type: cross Abstract: Most approaches for accelerating Markov chain mixing either rely on incorporating expensive geometric information in the proposals, or reduce the per-step cost of sampling via surrogate densities. We propose a localisation princip…

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