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English(EN) On skew-symmetric distributions and their use in Monte Carlo sampling algorithms: coordinate-free, Gibbs-style and manifold versions of the Barker proposal

新的Barker提案变体增强了蒙特卡洛采样算法

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员引入了Barker提案的新版本，这是一种利用斜对称分布改进马尔可夫链蒙特卡洛（MCMC）采样的Metropolis-Hastings算法。该论文详细介绍了该算法的无坐标、Gibbs风格和流形版本。数值实验表明，Gibbs风格变体提高了相关目标的采样效率，而简化的流形Barker算法在不规则目标几何形状上显示出优于MALA的显著优势。 AI

影响 引入了可能提高AI和机器学习中使用的采样方法效率和鲁棒性的新算法技术。

排序理由 该集群包含一篇详细介绍新算法方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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新的Barker提案变体增强了蒙特卡洛采样算法

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该集群包含一篇详细介绍新算法方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Minh Vu, Samuel Livingstone, Pantelis Samartsidis ·

    关于斜对称分布及其在蒙特卡洛采样算法中的应用：无坐标、Gibbs风格和流形版本的Barker提议

    arXiv:2610.01448v1 Announce Type: cross Abstract: Skew-symmetric probability distributions provide a principled mechanism for incorporating gradient information into Markov chain Monte Carlo algorithms. Here we review the (preconditioned) Barker proposal, a Metropolis--Hastings a…

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