Scoring Bayesian Networks of Mixed Variables.
Scoring Bayesian Networks of Mixed Variables.
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DOI：
10.1007/s41060-017-0085-7
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发表时间：
2018-08
影响因子：
2.4
通讯作者：
Cooper GF
中科院分区：
文献类型：
--
作者：
Andrews B;Ramsey J;Cooper GF
在本文中，我们概述了两个新的评分方法学习贝叶斯网络的存在下，连续和离散变量，即，混合变量。虽然在自动贝叶斯网络学习领域已经做了很多工作，但很少有研究在连续变量和离散变量的情况下研究这一任务，同时关注可扩展性。我们的目标是提供两个新的和可扩展的评分功能，能够处理混合变量。第一种方法，条件高斯（CG）得分，提供了一个非常有效的选择。第二种方法，混合变量多项式（MVP）评分，允许更广泛的建模关系，包括非线性，但它比CG慢。这两种方法都计算对数似然和自由度项，并将其纳入贝叶斯信息准则（BIC）评分。此外，我们引入了一个结构之前，有效地学习大型网络和简化评分的离散情况下，表现良好的经验。虽然这项工作的核心集中在搜索和得分范例中的应用，我们还展示了如何引入评分函数可以很容易地适应基于约束的贝叶斯网络学习算法的条件独立性测试。最后，我们描述了模拟混合变量类型的网络的方法，并在这种模拟上评估我们提出的方法。
In this paper we outline two novel scoring methods for learning Bayesian networks in the presence of both continuous and discrete variables, that is, mixed variables. While much work has been done in the domain of automated Bayesian network learning, few studies have investigated this task in the presence of both continuous and discrete variables while focusing on scalability. Our goal is to provide two novel and scalable scoring functions capable of handling mixed variables. The first method, the Conditional Gaussian (CG) score, provides a highly efficient option. The second method, the Mixed Variable Polynomial (MVP) score, allows for a wider range of modeled relationships, including non-linearity, but it is slower than CG. Both methods calculate log likelihood and degrees of freedom terms, which are incorporated into a Bayesian Information Criterion (BIC) score. Additionally, we introduce a structure prior for efficient learning of large networks and a simplification in scoring the discrete case which performs well empirically. While the core of this work focuses on applications in the search and score paradigm, we also show how the introduced scoring functions may be readily adapted as conditional independence tests for constraint-based Bayesian network learning algorithms. Lastly, we describe ways to simulate networks of mixed variable types and evaluate our proposed methods on such simulations.