Some feasible methods that can simplify the model structure and reduce the learning complexity are proposed and the application of these methods to the belief network is explained briefly .

并从实际出发，提出了几种可以简化模型结构、降低学习复杂性的可行方法，简要说明了这些方法在 网络 模型中的应用。

Belief Network Method in Uncertainty Management

不精确推理中的 信度 网络方法

Approximate Inference Algorithms of Belief Network ( 1 )

信度 网近似推理算法（上）

In the aspect of the network fault diagnosis we study the fast network fault diagnose with the application of the Bayesian Belief Network .

在网络故障诊断方面，本文采用贝叶斯 置信 网，研究了网络故障的快速发现问题。

Dynamic Causality Diagram has some advantages compared with Belief Network it is more convenience to represent causal relationship and the superiority is taken on especially in the Fault Diagnosing field .

但因果图与 信度 网相比又具有一些自己独特的优点，在不确定性知识间的因果关系表达更加方便，尤其在故障诊断领域更有独特优势。

An On-Line Structure Learning Algorithm of Belief Network

信度 网结构在线学习算法

For solving the intelligent prediction and control issue of network congestion the AI technology is introduced into the network management system and the belief network modeling of telecommunication network congestion and corresponding decision network modeling are present .

本文以四川德阳、达川、 宜宾电信网本地 网管系统的工程为背景，将人工智能技术引入网管系统，主要想解决网络阻塞的智能预测及控制决策问题。

Modeling with Bayesian belief network has been a powerful tool to solve many uncertainty problems .

使用 贝叶斯 网建模已成为解决许多不确定性问题的强有力工具。

Study on Transition from Causality Diagram to Belief Network

因果图向 信度 网转化的方法研究

Uncertainty knowledge representation can be divided into two parts . One of them is knowledge representation based on probability theory such as belief network model dynamic causality diagram model markov network model the method used in PROSPECTOR specialist system and so on .

不确定的知识表达可分为两大类：一类是基于概率的方法，包括 信度 网、因果图、马尔可夫网以及在PROSPECTOR中使用的方法等。

Considering the factor of alarm loss a symptom node layer is added to the improved bipartite graph model and a network fault propagation model using the three-layer belief network is proposed .

考虑了告警丢失因素，在改进的二分图模型中加入了隐含的症状节点层，提出了基于三层 因果 图的网络故障传播模型。

The Causality Diagram theory which adopted graphical expression of knowledge and direct causality intensity of causality overcomes some shortages in Belief Network and has evolved into a mixed causality diagram methodology coped with discrete and continuous variable .

在因果图理论中，采用了图形化和直接因果强度来表达知识和因果关系，它克服了 贝叶斯 网的一些不足，已经发展成了一个能够处理离散变量和连续变量的混合模型。

Application of belief network in pattern recognition classifier

置信度 网络在判别分析中的应用

A Multi-Level Belief Network Model for Knowledge Acquisition

知识获取的多层 证据 网络模型

Analysis of Quality Management Risk in Large Construction Projects Based on Bayesian Belief Network & A Case Study of Beijing-Shanghai High-Speed Railway Project

基于 贝叶斯 网络的建设项目质量管理风险因素分析&以京沪高速铁路建设项目为例

Bayesian Belief Network Model Learning Inference and Applications

贝叶斯 网模型的学习、推理和应用

Many methods are used to induct belief network structures from data .

通过很多方法，可以从数据学习 信度 网结构。

It is mainly of two kinds Naive Bayesian Classification and Bayesian Belief Network Classification .

它主要有两种分类方法：一种为朴素贝叶斯分类，另一种为贝叶斯 信念 网络分类。

Then introduced the basic concepts and background knowledge of Bayesian classifier by analysis several common Bayesian classifiers in order to propose an appropriate Bayesian belief network model and algorithm applied to specific scenarios .

之后介绍了贝叶斯分类器的基本概念和背景知识，通过对几种常见的贝叶斯分类器的分析，得出了一种适用于事后审计的贝叶斯 信念 网络模型和适用于具体场景的贝叶斯 信念网络算法。

Among of uncertainty knowledge representation and reasoning models based on probability theory belief network model is representative due to its rigorousness and consistence in theory the efficient computation mechanism and intuitive graphical expression of knowledge .

在基于概率论的不确定性推理理论中， 信度 网模型因其具有理论上的严格性和一致性，具有有效地局部计算机制和直观的图形化知识表达，正日益受到高度重视。

With its local computation each belief network node can be regarded as a processor .

由于其局部计算特性，每个 信度 网节点可视为一个处理器进行并行运算。

Combining fuzzy method and the Bayesian belief network a novel anomaly detection model is devised .

提出了一种新型异常检测 模型。

Among the probabilistic approaches Pearl 's belief network is the most representative due to its rigorousness and consistence in theory the efficient local computation mechanism and intuitive graphical expression of knowledge .

在概率方法中， 信度 网由于其理论的健壮性和一致性、有效的局部计算机制和直观的图形化知识表达方式而日益受到重视。

Algorithm to Transform Dynamic Causality Diagram Into Belief Network

因果图转换为 信度 网的算法

Experimental results indicate that the expanded model has better retrieval performance than the basic belief network model 's.

实验证明，扩展模型的检索性能 优于基本 模型。

Algorithm of Bayesian Belief Network Structure of Compressed Candidature

压缩候选的贝叶斯 信念 网络构造算法