BriefGPT.xyz
Aug, 2022
不确定性贝叶斯网络：从不完整数据中学习
Uncertain Bayesian Networks: Learning from Incomplete Data
HTML
PDF
Conrad D. Hougen, Lance M. Kaplan, Federico Cerutti, Alfred O. Hero III
TL;DR
本文旨在改进处理不确定贝叶斯网络的现有方法，通过使该方法能够学习其参数的分布，即条件概率，并基于各种查询的强度和经验确定置信度限制，从而在不完整的数据情况下学习参数的后验分布。
Abstract
When the historical data are limited, the
conditional probabilities
associated with the nodes of
bayesian networks
are uncertain and can be empirically estimated. Second order
→