基于模拟的对参数贝叶斯模型的先验知识提取
Florence Bockting1, Stefan T Radev2, Paul-Christian Bürkner3
1Department of Statistics, TU Dortmund University, Dortmund, Germany. florence.bockting@tu-dortmund.de.
Scientific reports
|July 27, 2024
概括
本研究介绍了一种新的基于模拟的方法,用于贝叶斯统计学中的预先诱导,有效地将各种专家知识转化为任何模型的预先分布. 该方法在各种统计模型和提取技术中被证明是稳健的和可适应的.
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:
- 贝叶斯统计学可以将先前的知识纳入模型.
- 预先诱导将领域专家的知识转化为先前的分发.
- 现有的方法难以整合各种专家知识格式.
研究的目的:
- 开发一种基于模拟的先前诱导方法.
- 有效地利用各种专家知识格式 (数据,统计,参数).
- 制定与专家预期一致的先前分布,无论模型如何.
主要方法:
- 一种基于模拟的方法,使用随机梯度下降.
- 任何参数先前分布的学习超参数.
- 可适应基于量子的,基于瞬间的和基于直方图的诱导.
主要成果:
- 该方法有效地学习了先前的分布超参数.
- 在线性,通用线性和层次模型中证明了有效性和稳定性.
- 这种方法在很大程度上独立于底层模型结构.
结论:
- 开发的方法提供了一种灵活而强大的解决方案,用于预先诱导.
- 它成功地整合了各种形式的专家知识.
- 适用于广泛的贝叶斯模型场景.
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