来自连续数据的贝叶斯网络学习算法比较。
A comparison of Bayesian network learning algorithms from continuous data.
作者信息
Fu Lawrence D, Tsamardinos Ioannis
机构信息
Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA.
出版信息
AMIA Annu Symp Proc. 2005;2005:960.
Learning a Bayesian network from data is an important problem in biomedicine for the automatic construction of decision support systems and inference of plausible causal relations. Most Bayesian network learning algorithms require discrete data; however discretization may impact the quality of the learned structure. In this project, we present a comparison of different approaches for learning from continuous data to identify the most promising one and to quantify the impact of discretization in Bayesian network learning.
从数据中学习贝叶斯网络是生物医学领域中的一个重要问题，可用于自动构建决策支持系统以及推断合理的因果关系。大多数贝叶斯网络学习算法需要离散数据；然而，离散化可能会影响所学习结构的质量。在本项目中，我们对从连续数据进行学习的不同方法进行了比较，以确定最有前景的方法，并量化离散化在贝叶斯网络学习中的影响。
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