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English(EN) Gaussian Approximation for Multivariate Martingale Sums from Uniformly Ergodic Markov Chains

开发了马尔可夫链的新高斯近似界

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了来自均匀遍历马尔可夫链的多元鞅差和的新高斯近似界。这些界限以高阶 Wasserstein 距离表示，在固定维度和阶数时达到 O(n^{-1/2}) 的最优速率。该方法引入了新技术来管理时间依赖性和高阶 Wasserstein 距离的复杂性，有可能在这些条件下的统计分析中提供更广泛的应用。 AI

影响 推进了用于分析复杂数据的统计方法，可能影响 AI 模型的训练和评估。

排序理由 学术论文在 arXiv 上发表，详细介绍了统计方法。[lever_c_demoted from research: ic=1 ai=0.4]

在 arXiv stat.ML 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

开发了马尔可夫链的新高斯近似界

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学术论文在 arXiv 上发表，详细介绍了统计方法。[lever_c_demoted from research: ic=1 ai=0.4]
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报道来源 [1]

  1. arXiv stat.ML TIER_1 English(EN) · Yixuan Zhang, Qiaomin Xie ·

    来自均匀遍历马尔可夫链的多元鞅和的高斯近似

    arXiv:2609.09480v1 Announce Type: cross Abstract: We develop Gaussian approximation bounds in higher-order Wasserstein distance $W_p$, $p\geq2$, for sums of multivariate martingale differences generated by a uniformly ergodic Markov chain. Under an $L^{(2+\eta)p}$-moment conditio…

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