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通过变量自编码器重新定义参数估计和共变量选择:只需一次运行就足够了

Jan Rohleff1, Freya Bachmann1, Uri Nahum2,3

  • 1Department of Mathematics and Statistics, University of Konstanz, Konstanz, Germany.

CPT: pharmacometrics & systems pharmacology
|November 4, 2025
PubMed
概括

本研究介绍了一种新的生成人工智能 (AI) 框架,使用变异自编码器 (VAE) 进行非线性混合效应 (NLME) 药量计 (PMX) 建模. 由人工智能驱动的VAE在一次运行中高效地自动化了共变量选择和参数估计.

关键词:
贝叶斯的推理 贝叶斯的推理选择的共变量选择.基于数据的建模.生成型的人工智能 (AI)机器学习是机器学习.非线性混合效应建模非线性混合效应建模参数估计的参数估计.变量自动编码器变量自动编码器

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