一个基于贝叶斯卷积神经网络的通用线性模型
Yeseul Jeon1, Won Chang2,3, Seonghyun Jeong1,4
1Department of Statistics and Data Science, Yonsei University, Seoul 03722, South Korea.
Biometrics
|June 18, 2024
概括
本研究引入了贝叶斯的方法,将卷积神经网络 (CNN) 与通用线性模型 (GLM) 结合起来. 这种方法提高了预测准确度,并允许在复杂的图像和空间数据分析中进行可解释的统计推理.
科学领域:
- 计算生物学是一种计算生物学.
- 统计建模 统计建模
- 机器学习是机器学习.
背景情况:
- 卷积神经网络 (CNN) 在图像和空间数据分析方面表现出色,但缺乏直接的统计推理.
- 传统的统计模型与CNN的复杂性和过度参数化作斗争,阻碍了解释和不确定性量化.
研究的目的:
- 开发一种贝叶斯式方法,将CNN集成到通用线性模型 (GLM) 框架内.
- 为了使精确的统计推断,包括共变效应估计和预测不确定性量化,复杂的数据.
主要方法:
- 在GLM中嵌入CNN,使用从最后一个隐藏层中提取的特征.
- 采用蒙特卡洛 (MC) 抛弃特征提取和装配集团GLM来考虑特征提取的不确定性.
- 将该方法应用于生物和流行病学数据集,包括疟疾发病率,脑瘤图像和fMRI数据.
主要成果:
- 与传统方法相比,预测和回归系数推断的准确性提高.
- 实现可解释系数分析和可靠的不确定性量化.
- 对各种高维,相关的数据集的成功应用.
结论:
- 提出的贝叶斯CNN-GLM框架为复杂,高维数据的可解释分析提供了一个强大的工具.
- 该方法为图像回归和相关数据分析提供了准确的贝叶斯推理.
- 这种方法显著提高了机器学习应用中的统计建模能力.
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