贝叶斯网络

基于多分片贝叶斯网络的多Agent系统模型表示

The Model Representation of Multi-Agent System Based Multiply Sectioned Bayesian Networks

基于动态贝叶斯网络的多Agent交互的模型表示

Model Representing of Multi-agent Interactions Based on Dynamic Bayesian Networks

基于贝叶斯网络的Web访问模式挖掘模型系统

A system model based on Bayesian network

基于贝叶斯网络的XML文档查询模型

Query Model of XML Document Based on Bayesian Network

最后，本文讨论了一种使用贝叶斯网络对半结构化的Web文档进行表示和分类的方法。

At last , we discuss a Bayesian Network model which can be used to classify semi-structured document .

基于贝叶斯网络的无结构化P2P资源搜索方法

A Bayesian Network-Based Search Method in Unstructured Peer-to-Peer Networks

基于数据库信息构建贝叶斯网络的GA方法

Constructing Bayesian Networks Based on Database by Using GA

基于贝叶斯网络理论的TAN分类器无向依赖扩展

Extending TAN Classifiers Using Undirected Graphical Models Based on Bayesian Network Theory

这一结果将有助于设计算法搜索G中的等价类，在用得分等价原则学习贝叶斯网络结构时，可以进行局部得分，从而减少所需的数据量，提高效率。

This result can help to search the equivalent classes of a dag and be used in the learning of Bayesian Network .

基于PCA的贝叶斯网络构造算法与应用

A Study and Application of Learning Bayesian Network from Data Approach Based on PCA

学习和研究通过Matlab建立贝叶斯网络安全评价模型的方法，结合实例建立贝叶斯网络评价模型，对实例进行分析得出结果。

Study and research the method of using matlab to establish bayesian network safety evaluation model , combined with practical examples .

一种基于决策图贝叶斯网络的强度Pareto进化算法

An Improving Strength Pareto Evolutionary Algorithm Based on Bayesian Network with Decision Graphs

主要介绍了贝叶斯网络分类器中的TAN分类器的模型、构造方法及分类方法。

This paper mainly introduces the TAN classifier model , its building method and class method .

基于贝叶斯网络的Peer-to-Peer识别方法

Peer-to-Peer Traffic Identification Using Bayesian Networks

基于CBR的贝叶斯网络构造

Bayesian Networks Construction Based on CBR

然后设计了贝叶斯网络故障诊断系统，并用c语言实现了从建造贝叶斯网络结构，到进行诊断推理得出诊断结论整个过程。

Then the author designed a Bayesian network fault diagnosis system , and using c # language realized the process from the construction of Bayesian network to a diagnostic inference drawn conclusions .

结合D-S证据推理的贝叶斯网络法在配电网可靠性评估中的应用

Reliability Evaluation of Power Distribution System Based on D-S Evidence Inference and Bayesian Networks Method

常用的数据挖掘方法有许多，贝叶斯网络（BayesianNetworks，BN）方法在数据挖掘中的应用是当前研究的热点问题，具有广阔的应用前景。

Many methods have been used in data mining , Bayesian networks has become a focus currently . It has the broad application prospects .

贝叶斯网络的研究主要在贝叶斯网络的学习上，特别是贝叶斯网络的结构学习，而现在的研究表明贝叶斯网络的结构学习是NP问题，因此有效的结构学习方法的探寻是贝叶斯网络理论研究的重点。

Bayesian network research mainly focused on the learning of Bayesian networks , especially the structure learning of Bayesian network .

net和XML技术实现了贝叶斯网络综合实验平台的构建，对上述算法加以实现，并应用到电力系统可靠性评估中，取得了比较好的效果。

This paper also builds an integrated experimental platform for the test and further research of Bayesian Networks , and the improved algorithm is applied on the reliability assessment of power system .

在贝叶斯网络构造过程中，结合采用K2和MC-MC算法构建网络。

During the process of Bayesian networks modeling , K2 and MCMC algorithms are utilized together .

基于OWA算子的贝叶斯网络空地战场威胁评估研究

Study on Threat Evaluation Based on OWA Operator and Bayesian Network

采用贝叶斯网络（BayesianNets）和粒度层次（GranularityHierarchies）方法构建了覆盖型学生模型，为知识前测的出题策略和系统的学生适应性学习提供了依据。

We constructed student model with Bayesian Nets and Granularity Hierarchies method . They provide basis of strategy of test and student 's adaptive learning .

由于建立好的贝叶斯网络学生模型中存在无向环，使学生模型的推理成为一个NP难题。

However , reasoning on student model is a NP hard question in fact , since undirected loops are embedded in the Bayesian student model .

改进了关于图模结构学习中常见的MCMC算法。将这种方法构造的贝叶斯网络作为马尔可夫链初始状态的网络结构，利用改进后的MCMC算法，构造一个关于贝叶斯网络结构的马尔可夫链。

The traditional Markov Chain Monte Carlo method for structural learning in graphical models MCMC algorithm is improved .

利用从济南地区ETM图像中提取出的灰度共生纹理特征向量和灰度共生-差分维数向量对BP神经网络和朴素贝叶斯网络进行训练，并用训练后的网络对其它地区遥感图像进行地物识别实验。

In this article gray co-occurrence vector and co-dimension feature vector extracted from ETM images of Jinan are used in recognition experiment with BP neural network and Bayesian network .

通过与传统的混合高斯模型（GMM）的实验类比，该贝叶斯网络模型能够在同样的训练和测试数据情况下识别率平均相对提高30%。

Experimental results under the same train and test data show that this network structure can achieve thirty percent average relative improvement in performance than classical Gaussian mixture model .

电力变压器数据信息具有不完备、不确定性的缺点，而贝叶斯网络有知识表达灵活、分析处理不确定性与关联性问题能力强的优点，本文提出一种基于Multi-Agent的变压器故障诊断模型。

Due to the shortcomings of the randomness and uncertainty of power transformer fault diagnosis data , the benefits of the Bayesian network classifiers and the features of multi-Agent , this paper introduces a multi-Agent system diagnosis model .

把观测得到的各MIB变量的信息经由贝叶斯网络加以融合，从而计算出网络出现故障的概率，包括未知的和不可预见的故障。

Then residuals of multiple MIB variables are combined in the probabilistic framework of a Bayesian network to compute the probability of fault , including unknown or unpredictable faults .

针对多阶段系统（PMS）的可靠性评估问题，提出了一种基于贝叶斯网络（BN）的可靠性分析模型PMS-BN。

The paper presents a Bayesian networks ( BN ) framework for the reliability analysis of phased-mission systems ( PMS ), named PMS-BN model .