通过使用 THAMES 估计器轻松计算后期模拟的边际概率
Martin Metodiev1,2, Marie Perrot-Dockès2, Sarah Ouadah3
1Université Clermont Auvergne, Laboratoire de Mathématiques Blaise Pascal.
概括
我们开发了一种新的,高效的方法,使用现有的后置模拟来估计边际概率. 这种方法简化了计算,为贝叶斯推理提供了公正和一致的结果.
科学领域:
- 统计 统计 统计 统计
- 计算统计学 计算统计学
- 贝叶斯的推理是贝叶斯的推理.
背景情况:
- 估计边际概率对于贝叶斯分析中的模型选择至关重要.
- 现有的方法可能是计算密集型或需要额外的模拟.
- 需要有效且易于使用的估计器.
研究的目的:
- 为边际概率提出一种新的,易于计算的估计器.
- 结合和改进现有的相互重要抽样技术.
- 为贝叶斯模型比较提供一个计算效率高的工具.
主要方法:
- 使用反重量抽样与非正常后密度.
- 在DiCiccio等人的工作基础上. (1997年) 和罗伯特和幽灵 (2009年).
- 结合一个简单的蒙特卡洛近似限制参数空间.
主要成果:
- 拟议的估计器对于边际概率的反面是不偏见的.
- 估计器证明了一致性,有限的方差和非对称的正常性.
- 导出了指定用户定义的控制参数的最佳方法.
结论:
- 新的估计器为边际概率估计提供了一种计算效率高且统计学上合理的方法.
- 它通过利用现有的后置模拟输出来简化贝叶斯推理.
- 该方法是稳固的,可以适应受约束和不受约束的参数空间.
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