贝叶斯网络是在不确定性环境下有效的知识表示方式和概率推理模型，是一种流行的图形决策化分析工具。
Bayesian Networks is a model that efficiently represents knowledge and probabilistic inference and is a popular graphics decision-making analysis tool.
贝叶斯网络是数据采掘的一个非常有效的工具，它能够定性和定量地分析属性之间的依赖关系，进行概率推理。
Bayesian network as, a very useful tool in data mining, can provide qualitative and quantitative relationship between attributes and probability inference.
所提出计算模型为贝叶斯网的概率推理提供了一种新的局部计算方法。
The proposed computation models will supply new local computation methods for Bayesian network probabilistic inferences.
同时利用贝叶斯网络实现概率推理，便于描述故障特征的变化及对变压器故障原因的快速分析。
At the same time, probability reasoning can be realized by BN, which can be used to describe changes of fault symptoms and analyze fault reasons of transformer.
贝叶斯方法的特点是使用概率去表示所有形式的不确定性，学习或其他形式的推理都用概率规则来实现。
The characteristic of the Bayes method is to use probability to express the uncertainty of all forms, learning and the reasoning of other forms are all realized with the rule of probability.
用贝叶斯推理问题为实验材料，探讨了主体关联性对贝叶斯推理概率估计的影响。
Bayesian tasks were used in the present study to explore the influence of subject relevancy on Bayesian reasoning.
贝叶斯学习理论使用概率去表示所有形式的不确定性，通过概率规则来实现学习和推理过程。
Bayesian learning Theory represents uncertainty with probability and learning and inference are realized by probabilistic rules.
本文主要涉及的不确定推理模型包括主观贝叶斯的概率推理模型，可信度理论推理模型，证据理论及其改进推理模型以及神经网络推理模型。
In the paper, the models of uncertain reasoning are focused, such as the reasoning model of Bayes probability, Reliability theory, D-S evidence theory and Neural Network.
贝叶斯学习是一种基于已知的概率分布和观察到的数据进行推理，做出最优决策的概率手段。
Bayesian learning is a probability method that makes optimal decision based on known probability distribution and recently observed data.
在控制系统中，将贝叶斯概率引入到模糊rbf神经网络中，增强了系统的推理能力，提高了飞机各个航道位置的模拟伺服精度。
In the control system, Bayes probability is introduced in the fuzzy RBF neural network and it intensity the inference ability and increase the servo precision.
针对目标综合识别过程中的复杂性和不确定性特点，利用贝叶斯网络融合模型对融合识别过程进行概率建模及推理。
For dealing with the complexity and uncertainty in the target identification fusion, the fusion model of Bayesian Networks is used.
针对目标综合识别过程中的复杂性和不确定性特点，利用贝叶斯网络融合模型对融合识别过程进行概率建模及推理。
For dealing with the complexity and uncertainty in the target identification fusion, the fusion model of Bayesian Networks is used.
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