可分解模型中的有效逐步选择
Efficient Stepwise Selection in Decomposable Models
摘要：
在本文中，我们提出了一种有效的方法进行逐步选择类的可分解模型。本文的主要贡献是一个简单的特征的边缘，可以添加到一个可分解的模型，同时保持所得到的模型可分解和有效的算法，枚举所有这样的边缘为一个给定的模型在基本上O（1）时间每个边缘。我们还讨论了如何使用我们的数据结构有效地执行向后选择。我们还分析了完整的逐步选择过程的复杂性，包括选择哪些符合条件的边缘添加到（或删除）当前模型的复杂性，目的是最小化所得到的模型与数据饱和模型的Kullback-Leibler距离。
英文摘要：
In this paper, we present an efficient way of performing stepwise selection in the class of decomposable models. The main contribution of the paper is a simple characterization of the edges that canbe added to a decomposable model while keeping the resulting model decomposable and an efficient algorithm for enumerating all such edges for a given model in essentially O(1) time per edge. We also discuss how backward selection can be performed efficiently using our data structures.We also analyze the complexity of the complete stepwise selection procedure, including the complexity of choosing which of the eligible dges to add to (or delete from) the current model, with the aim ofminimizing the Kullback-Leibler distance of the resulting model from the saturated model for the data.
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。
