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English(EN) Anytime-valid simulation-based hypothesis testing

新论文提出无需解析密度即可进行随时有效的假设检验

本文介绍了一种在无密度设置下进行假设检验的新方法,其中仅可获得来自两个概率分布的模拟数据。作者构建了一个e-test鞅,以开发一种顺序检验,该检验提供随时有效的I类错误保证。该方法还提供了近似增长最优性和几何衰减的II类错误界限,最终实现渐近功效一。本文的贡献是为基于仿真的顺序假设检验提供了一种简洁有效且无需解析密度的解决方案。 AI

影响 引入了一种分析模拟数据的新型统计方法,可能影响那些依赖于有限或无解析密度信息的假设检验的AI研究。

排序理由 该项目是一篇研究论文,详细介绍了一种新的假设检验方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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

新论文提出无需解析密度即可进行随时有效的假设检验

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该项目是一篇研究论文,详细介绍了一种新的假设检验方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    随时有效的基于仿真的假设检验

    For a given data distribution $(X_t)_{t \in \mathbb{N}} \sim Q$ i.i.d., we investigate the hypothesis testing problem: $H_0: Q = P_0$ vs. $H_1: Q = P_1$, for two different model probability distributions $P_0$ and $P_1$. In contrast to the standard setting, where analytic densiti…