基于贝叶斯网络的一种牛奶产量预测研究

本文运用贝叶斯网络对某农场的牛奶产量进行学习与预测,运用Chi2离散化方法的一种变形进行数据预处理,采用有启发规则指导的、带随机重启的贪心算法搜索网络结构;最后,将贝叶斯网络方法的结果与多元线性回归方法得到的结果进行了比较。     

Applications of Bayesian belief networks in water resource management: A systematic review

Bayesian belief networks (BBNs) are probabilistic graphical models that can capture and integrate both quantitative and qualitative data, thus accommodating data-limited conditions. This paper systematically reviews applications of BBNs with respect to spatial factors, water domains, and the consideration of climate change impacts. The methods used for constructing and validating BBN models, and their applications in different forms of decision-making support are examined. Most reviewed publications originate from developed countries (70%), in temperate climate zones (42%), and focus mainly on water quality (42%). In 60% of the reviewed applications model validation was based on the expert or stakeholder evaluation and sensitivity analysis, and whilst in 27% model performance was not discussed. Most reviewed articles applied BBNs in strategic decision-making contexts (52%). Integrated modelling tools for addressing challenges of dynamically complex systems were also reviewed by analysing the strengths and weaknesses of BBNs, and integration of BBNs with other modelling tools.     

采用贝叶斯网络建立一种智能学习环境学生代理模型的研究

贝叶斯（Bayesian）网络近年成为智能代理引人注目的研究方向,本文在介绍Bayesian网络及其构建方法、学习方法的基础上,构建了一个通用的可扩展的智能学习环境中的学生模型。     

基于贝叶斯网络的电信客户流失预测分析

电信客户流失分析常用的数据挖掘方法有自动聚类、决策树和人工神经网络,它们是采用数据本身来训练模型的,没有利用先验知识。电信客户流失是由客户心理、服务质量和对手竞争等诸多复杂的因素造成的,利用这些已有的先验知识,可以提高预测的精度。该文根据先验知识选取分析变量,采集样本数据,通过贝叶斯网络的结构学习和参数学习,建立客户流失模型并进行客户流失趋势预测,取得了比标准数据集更准确的结果,该结果和决策树方法的预测结果相比还具有较大的优势,说明贝叶斯网络是分析客户流失等不确定性问题的有效工具。