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关于贝叶斯地区建设的一些问题,保证覆盖范围
Michael Evans1, Miaoshiqi Liu1, Michael Moon1
1Department of Statistical Sciences, University of Toronto, 700 University Ave, Toronto, ON M5G 1Z5 Canada.
概括
本研究引入了贝叶斯的方法,用于构建具有对感兴趣的参数有保证覆盖概率的区域. 该方法可以控制先前的覆盖范围和准确性,确保可靠的推断,避免不合适的区域.
科学领域:
- 统计 统计 统计 统计
- 贝叶斯的推理是贝叶斯的推理.
- 统计建模 统计建模
背景情况:
- 构建具有保证覆盖概率的可靠统计区域是一个持续的挑战.
- 现有的信任区域可能会受到不良属性的影响,例如不合适的区域.
研究的目的:
- 开发一个贝叶斯学方法来创建具有对感兴趣的参数有保证覆盖概率的区域.
- 提供一种方法来控制先前覆盖概率和贝叶斯置信区间的准确性.
- 解决现有方法的局限性,特别是不合适区域的问题.
主要方法:
- 使用贝叶斯原则来定义感兴趣的区域.
- 使用条件先验集成出麻烦参数.
- 通过调整样本大小来控制覆盖率和准确性.
主要成果:
- 发达地区本质上是贝叶斯的,提供贝叶斯的信心.
- 通过样本大小,可以精确控制先前覆盖率和准确性.
- 该方法保证避免不合适的区域,这是其他方法中常见的问题.
结论:
- 建议的贝叶斯框架为构建可靠的统计区域提供了一个强大的方法.
- 这种方法通过控制关键绩效指标来提高统计推断的可靠性.
- 它比容易产生不合适区域的方法提供了显著的改进.
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