一个贝叶斯非参数模型,用于单元球的正方位上的边界定向数据
Emiliano Geneyro1, Gabriel Núñez-Antonio1
1Departamento de Matemáticas, Universidad Autónoma Metropolitana-Unidad Iztapalapa, Mexico City, Mexico.
Journal of applied statistics
|February 28, 2024
概括
本研究引入了一种新的贝叶斯模型用于定向数据分析,特别是对于局限于第一个方位的变量. 非参数模型使用迪里克莱特过程混合物和预测的马密度来进行可靠的统计推理.
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.
- 指向数据分析指向数据分析
背景情况:
- 定向数据在各种研究领域中普遍存在.
- 分析局限于K维单元球的子集的定向变量,例如正方位,存在重大挑战.
- 目前用于分析边界子集上的定向数据的方法有限.
研究的目的:
- 介绍一个新的非参数贝叶斯模型,用于局限于第一个方向变量.
- 解决分析K维单元球的边界子集上的定向数据的方法缺口.
- 为此类数据提供灵活和强大的建模框架.
主要方法:
- 开发一个非参数贝叶斯模型.
- 使用迪里克莱特过程混合模型作为核心框架.
- 采用多变量预测的马密度作为内核分布.
- 实施切片采样方案用于模型推理.
主要成果:
- 拟议的模型有效地描述了局限于第一个方向变量的方向变量.
- 切片采样推断方案被证明是计算上可行的.
- 该方法通过模拟和现实世界的数据应用来验证.
结论:
- 引入的贝叶斯模型提供了一个有价值的工具,用于分析方向数据在第一个 orthant.
- 这项工作有助于开发用于边界分流体上的定向数据的统计方法.
- 该模型为涉及有限的定向变量的应用提供了灵活和可解释的方法.
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