贝叶斯网络学习：知识与统计数据的结合
Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
摘要：
我们描述了学习贝叶斯网络的用户知识和统计数据的组合算法。该算法有两个组成部分：评分度量和搜索过程。评分度量采用网络结构、统计数据和用户的先验知识，并返回与给定数据的网络结构的后验概率成比例的分数。搜索过程生成网络，用于通过评分度量进行评估。我们的贡献是三方面的。首先，我们确定了度量的两个重要性质，我们称之为事件等价性和参数模块性。这些属性大多被忽略了，但是当它们结合在一起时，极大地简化了用户先验知识的编码。特别是，用户可以表达她的知识-大部分-作为一个单一的先验贝叶斯网络的域。其次，我们描述了本地搜索和退火算法与评分指标一起使用。在每个节点最多有一个父节点的特殊情况下，我们证明了启发式搜索可以用多项式算法来代替，以确定网络的最高分数。第三，我们描述了评估贝叶斯网络学习算法的方法。我们应用这种方法来比较指标和搜索程序。
英文摘要：
We describe algorithms for learning Bayesian networks from a combination of user knowledge and statistical data. The algorithms have two components: a scoring metric and a search procedure. The scoring metric takes a network structure, statistical data, and a user's prior knowledge, and returns a score proportional to the posterior probability of the network structure given the data. The search procedure generates networks for evaluation by the scoring metric. Our contributions are threefold. First, we identify two important properties of metrics, which we call event equivalence and parameter modularity. These properties have been mostly ignored, but when combined, greatly simplify the encoding of a user's prior knowledge. In particular, a user can express her knowledge-for the most part-as a single prior Bayesian network for the domain. Second, we describe local search and annealing algorithms to be used in conjunction with scoring metrics. In the special case where each node has at most one parent, we show that heuristic search can be replaced with a polynomial algorithm to identify the networks with the highest score. Third, we describe a methodology for evaluating Bayesian-network learning algorithms. We apply this approach to a comparison of metrics and search procedures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
