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.
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.
概率逻辑是用逻辑推理的方法解决因随机性引起的不确定性推理问题。
Probabilistic logic makes use of the method of logic reasoning to deal with the uncertain reasoning, which is cause by randomicity.
该推理模型前级采用神经网络并行子网，用于目标的预分类，后级采用证据理论用于多周期的不确定性推理和概率的全局分配。
The forestage of the fusion model completes target presort and its post-stage is used to multi-period uncertainty inference and the whole set distribution of probability.
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.
概率论是表示不确定性的唯一合理的方法，概率论对于机器学习或不确定情况下的推理是有用的。
Probability theory is the only reasonable way to represent uncertainty, it is useful for machine learning or reasoning under uncertainty.
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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