论文标题
混合贝叶斯网络的结构学习
Structure Learning for Hybrid Bayesian Networks
论文作者
论文摘要
贝叶斯网络已被用作以灵活但可解释的方式表示多个随机变量的联合分布的机制。学习贝叶斯网络结构的一个主要挑战是如何建模网络，其中包括连续和离散的随机变量（称为混合贝叶斯网络）的混合物。本文概述了有关处理混合贝叶斯网络的方法的文献。通常采用两种方法之一：数据被认为具有连接分布，该分布是为离散和连续变量混合而设计的，或者连续随机变量被离散化，从而产生离散的贝叶斯网络。在本文中，我们提出了一种将所有随机变量建模为高斯的策略，将其称为{\它将其作为高斯（rag）}运行。我们证明，与将连续的随机变量转换为离散相比，抹布从理论上和模拟研究中对图形结构进行了更可靠的估计。这两种策略也在儿童肥胖数据集上实施。两种不同的策略在最佳图结构上产生了显着差异，模拟研究的结果表明我们的策略更可靠。
Bayesian networks have been used as a mechanism to represent the joint distribution of multiple random variables in a flexible yet interpretable manner. One major challenge in learning the structure of a Bayesian network is how to model networks which include a mixture of continuous and discrete random variables, known as hybrid Bayesian networks. This paper overviews the literature on approaches to handle hybrid Bayesian networks. Typically one of two approaches is taken: either the data are considered to have a joint distribution which is designed for a mixture of discrete and continuous variables, or continuous random variables are discretized, resulting in discrete Bayesian networks. In this paper, we propose a strategy to model all random variables as Gaussian, referred to it as {\it Run it As Gaussian (RAG)}. We demonstrate that RAG results in more reliable estimates of graph structures theoretically and by simulation studies, than converting continuous random variables to discrete. Both strategies are also implemented on a childhood obesity data set. The two different strategies give rise to significant differences in the optimal graph structures, with the results of the simulation study suggesting that our strategy is more reliable.
