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Updated: Jul 15, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

998

通过K-L扩展和贝叶斯深度神经网络的高维动态结构系统的时间变量不确定性传播方法

Jingfei Liu1,2, Chao Jiang3, Haibo Liu4

  • 1Henan Key Laboratory of Superhard Abrasives and Grinding Equipment, Henan University of Technology, Zhengzhou 450001, People's Republic of China.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
|September 24, 2023
PubMed
概括

一种新的时间变量不确定性传播 (TUP) 方法有效地处理具有许多输入的动态结构系统. 这种方法使用任意随机过程模拟 (ASPS) 和贝叶斯深度神经网络进行准确的不确定性分析.

关键词:
贝叶斯深度神经网络是贝叶斯深度神经网络.随意的随机过程模拟过程模拟.动态结构系统是一个动态的结构系统.高维问题是高维的问题.不确定性的传播.

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