English(EN) Empirical concentration for chains with infinite memory

新方法为无限记忆链提供经验集中界限

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-26 04:00

研究人员开发了一种新方法，用于为具有无限记忆的平稳链建立经验集中界限，超越了传统的马尔可夫链框架。该方法从单个轨迹估计未知的依赖量，适用于有限和连续状态空间。导出的置信界限在渐近上与理论界限一样紧密，并且保持自适应性，不需要事先了解常数。 AI

影响这项研究通过为时间序列分析提供更紧密的置信界限，有可能提高学习算法的可靠性。

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

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arXiv stat.ML TIER_1 English(EN) · Pierre Alquier, Paul Doukhan · 2026-10-26 04:00

具有无限记忆链的经验集中

arXiv:2610.32265v1 Announce Type: new Abstract: Concentration inequalities for time series usually involve unknown quantities measuring dependence. This prevents their direct use as confidence bounds, or as criteria for learning algorithms. Recent works proved empirical concentra…