文档介绍：因子分析中的贝叶斯网络方法的研究 中文摘要
因子分析中的贝叶斯网络方法的研究
中文摘要
贝叶斯网络作为一种表示概率空间的模型，是对不确定问题模拟和
推理的一种有效的工具，它利用一组随机变量之间的条件独立关系减少
了对这组随机变量的联合概率分布进行表示时所需要的参数个数。其具
有根据信息的变化而变化的能力，可以综合数据和专家先验知识来识别
变量之间潜在的联系和相关程度。贝叶斯网络结构的学****就是寻找与已
知数据及先验知识最匹配的网络结构图形。贝叶斯网络在实际问题中有
广泛的应用，本文给出用贝叶斯网络方法进行因子分析的方法，并通过
实例分析证明贝叶斯网络方法得到的结果能优于或相当于传统的因子
分析方法的结果。同时由于其网络图形的形象性和直观性，贝叶斯网络
方法作为非线性的因子分析，与传统因子分析方法相比较，有更广泛的
应用，值得我们研究。文章最后对贝叶斯网络的分类进行深一步的讨论。
关键词：贝叶斯网络, MCMC 方法, 互信息, 结构学****br/>
作 者：王 婷
指导老师：汪四水（副教授）
I
因子分析中的贝叶斯网络方法的研究 Abstract
Factor Analysis of Bayesian network method
Abstract
As a probability model, Bayesian network is an effective tool for the
uncertainty of the simulation and reasoning, which make use of conditional
independent relationship among a set of random variables to reduce the
number of the parameters that are needed for the joint probability. With the
ability of changing with the new information, Bayesian network can be
integrated by data and prior knowledge of experts, and then identify potential
links and correlation among variables. The purpose of finding Bayesian
network structure is to seek the structure that matches with data and prior
knowledge well. Bayesian network is practical in a wide range of
applications. In this paper, Bayesian network method is compared with the
traditional factor analysis，and example shows the result of Bayesian network
method can be better than or equivalent to the traditional factor analysis. On
the other hand, as a kind of nonlinear factor analysis, Bayesian network
method has a much wider a