用于贝叶斯学习的Python环境：从知识和数据中推断贝叶斯网络的结构
Python Environment for Bayesian Learning: Inferring the Structure of Bayesian Networks from Knowledge and Data.
作者信息
Shah Abhik, Woolf Peter
机构信息
Department of Chemical Engineering, 3320 G.G. Brown, Ann Arbor, MI 48103, USA.
出版信息
J Mach Learn Res. 2009 Jun 1;10:159-162.
Abstract
In this paper, we introduce pebl, a Python library and application for learning Bayesian network structure from data and prior knowledge that provides features unmatched by alternative software packages: the ability to use interventional data, flexible specification of structural priors, modeling with hidden variables and exploitation of parallel processing.
摘要
在本文中，我们介绍了pebl，这是一个用于从数据和先验知识中学习贝叶斯网络结构的Python库及应用程序，它提供了其他软件包所没有的功能：使用干预数据的能力、结构先验的灵活指定、对隐藏变量进行建模以及利用并行处理。
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