测试超马丁加尔信心区间的表现对于伯努利试验的成功概率
Peter Wills1, Emanuel Knill2,3, Kevin Coakley2
1Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO 80309 USA.
概括
测试超级马丁加尔为拒绝零假设提供了一种强大的方法,特别是在小的p值下,并且可以证明量子纠. 这项研究量化了与他们的灵活停车规则相关的成本.
科学领域:
- 物理学的基础 物理学的基础
- 量子信息科学 量子信息科学
- 统计推理 统计推理
背景情况:
- 测试超级马丁加尔是用于拒绝复合零假设的非负超级马丁加尔.
- 当它们的值很大时,它们提供了反对零假设的证据,特别是小的p值.
- 应用包括在贝尔测试中拒绝局部现实主义和认证量子纠.
研究的目的:
- 为了评估测试超级马丁加尔的性能,用于计算p值和置信区间.
- 为了比较测试超级马丁加尔与切尔诺夫-霍夫丁极限和确切的p值.
- 量化与测试超级马丁加尔的任意停止规则相关的成本.
主要方法:
- 使用测试超级马丁作为测试假设的统计工具.
- 应用接受区域的反转来确定置信集.
- 使用推断在伯努利试验中的成功概率的一个例子来比较性能.
主要成果:
- 测试超级马丁加尔对于拒绝虚假假设和验证纠等现象是有效的.
- 该研究量化了使用无限制停止规则的测试超级马丁加尔所产生的成本.
- 与现有方法 (如切诺夫-霍夫丁边界) 进行性能比较.
结论:
- 测试超级马丁加尔是适应和有效的假设测试和信心区间的建设.
- 随意停车规则的灵活性带来了可量化的成本.
- 这些方法在物理学和量子信息科学中是有价值的,用于严格的推断.
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