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强化借款框架:利用辅助数据进行个性化推理.

Ziyu Ji1, Julian Wolfson1

  • 1Division of Biostatistics & Health Data Science, School of Public Health, University of Minnesota, Minneapolis, Minnesota, USA.

Statistics in medicine
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概括
此摘要是机器生成的。

研究人员使用辅助数据开发了一个用于个性化推断的新框架. 强化借款框架 (RBF) 提高了准确性,并减少了与现有方法相比的错误,计算成本最小.

关键词:
贝叶斯的方法 贝叶斯的方法个性化的推理推理.多源数据借用多源数据借用补充数据 补充数据

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科学领域:

  • 统计建模 统计建模
  • 数据科学是数据科学.
  • 机器学习 机器学习

背景情况:

  • 辅助数据越来越多地用于增强个性化的推断.
  • 现有的方法,如多源可交换性模型 (MEM),基于参数可交换性,从补充来源借取信息.
  • 这些方法往往忽略了其他有价值的信息,这些信息可以确定源代换性.

研究的目的:

  • 提出一个通用的强化借贷框架 (RBF),以加强个性化的推断.
  • 利用辅助数据,包括距离嵌入的先前数据,以加强对目标参数的推断.
  • 为了提高推断准确度,最小的计算开销.

主要方法:

  • 制定了一个通用的强化借贷框架 (RBF).
  • 整合了远程嵌入式预先利用目标参数之外的辅助信息.
  • 应用RBF来分析COVID-19大流行对个人行为的影响.

主要成果:

  • 与现有方法相比,RBF实现了比现有方法低20%至40%的平均平方误差 (MSE).
  • 该框架有效地利用了各种辅助信息来源.
  • 证明了改进的个性化推断,最小的额外计算负担.

结论:

  • 拟议的RBF对现有的个性化推断方法提供了显著的改进.
  • 通过考虑更广泛的信息范围,RBF提高了辅助数据的利用率.
  • 这一框架具有实际应用,如COVID-19行为研究所示.