Dependency networks for inference, collaborative filtering, and data visualization
Dependency networks for inference, collaborative filtering, and data visualization
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DOI：
10.1162/153244301753344614
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发表时间：
2001-12-01
影响因子：
6
通讯作者：
Kadie, C
中科院分区：
文献类型：
--
作者：
Heckerman, D;Chickering, DM;Kadie, C
我们描述了概率关系的图形模型（贝叶斯网络的替代方案），称为依赖网络。与贝叶斯网络不同，依赖网络的图可能是循环的。依赖网络的概率组件（如贝叶斯网络）是一组条件分布，每个节点对应给定其父节点。我们确定了这种表示的几个基本属性，并描述了一种从数据中学习图形和概率分量的计算有效的过程。我们描述了这种表示在概率推理、协作过滤（预测偏好的任务）和非因果预测关系的可视化中的应用。
We describe a graphical model for probabilistic relationships-an alternative to the Bayesian network-called a dependency network. The graph of a dependency network, unlike a Bayesian network, is potentially cyclic. The probability component of a dependency network, like a Bayesian network, is a set of conditional distributions, one for each node given its parents. We identify several basic properties of this representation and describe a computationally efficient procedure for learning the graph and probability components from data. We describe the application of this representation to probabilistic inference, collaborative filtering (the task of predicting preferences), and the visualization of acausal predictive relationships.