Learning Bayesian Networks
Learning Bayesian Networks
复制标题
学习贝叶斯网络
DOI：
10.4018/978-1-60566-010-3.ch174
复制
发表时间：
2009
影响因子：
29.4
通讯作者：
P. Sebastiani
中科院分区：
文献类型：
--
作者：
M. Ramoni;P. Sebastiani
贝叶斯网络诞生于人工智能、统计学和概率论的交叉点（Pearl, 1988），是知识发现和数据挖掘前沿的一种表示形式主义（Heckerman, 1997）。贝叶斯网络属于更一般的一类模型，称为概率图形模型（Whittaker, 1990; Lauritzen, 1996），它是图论和概率论的结合，它们的成功取决于它们通过将复杂的概率模型分解成更小的、可处理的组件来处理复杂概率模型的能力。概率图模型由图定义，其中节点表示随机变量，弧表示这些变量之间的依赖关系。这些弧线由概率分布标注，形成了链接变量之间的相互作用。概率图模型称为贝叶斯网络，其中连接其变量的图为有向无环图（DAG）。该图表示用于分解网络变量联合概率分布的条件独立假设，从而使从大型数据库学习的过程易于计算。从数据中推导出的贝叶斯网络可以用来研究变量之间的远距离关系，也可以通过计算一个变量的条件概率分布，给出其他一些变量的值来进行预测和解释。
Born at the intersection of artificial intelligence, statistics, and probability, Bayesian networks (Pearl, 1988) are a representation formalism at the cutting edge of knowledge discovery and data mining (Heckerman, 1997). Bayesian networks belong to a more general class of models called probabilistic graphical models (Whittaker, 1990; Lauritzen, 1996) that arise from the combination of graph theory and probability theory, and their success rests on their ability to handle complex probabilistic models by decomposing them into smaller, amenable components. A probabilistic graphical model is defined by a graph, where nodes represent stochastic variables and arcs represent dependencies among such variables. These arcs are annotated by probability distribution shaping the interaction between the linked variables. A probabilistic graphical model is called a Bayesian network, when the graph connecting its variables is a directed acyclic graph (DAG). This graph represents conditional independence assumptions that are used to factorize the joint probability distribution of the network variables, thus making the process of learning from a large database amenable to computations. A Bayesian network induced from data can be used to investigate distant relationships between variables, as well as making prediction and explanation, by computing the conditional probability distribution of one variable, given the values of some others.