Bayesian CART model search
Bayesian CART model search
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DOI：
10.2307/2669832
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发表时间：
1998-09-01
影响因子：
3.7
通讯作者：
McCulloch, RE
中科院分区：
文献类型：
--
作者：
Chipman, HA;George, EI;McCulloch, RE
在本文中，我们提出了一种寻找分类回归树(CART)模型的贝叶斯方法。该方法的两个基本组成部分是先验规范和随机搜索。其基本思想是让先验信息诱导出一个后验分布，从而引导随机搜索朝着更有前景的购物车模型前进。随着搜索的进行，可以用各种标准来选择这样的模型，例如后验概率、边际似然、残差平方和或错分率。通过实例说明了该方法相对于其他方法的潜在优势。
In this article we put forward a Bayesian approach for finding classification and regression tree (CART) models. The two basic components of this approach consist of prior specification and stochastic search. The basic idea is to have the prior induce a posterior distribution that will guide the stochastic search toward more promising CART models. As the search proceeds, such models can then be selected with a variety of criteria, such as posterior probability, marginal likelihood, residual sum of squares or misclassification rates. Examples are used to illustrate the potential superiority of this approach over alternative methods.