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dynamic bayesian network
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  动态贝叶斯网络
     As for the methods of evaluating the exact risks, the author has designed the quantification models for credit and operating risks basing on Bayesian Network, as well as the prewarning quantification models for credit and operating risks basing on dynamic Bayesian Network.
     在数理模型的使用上,关于具体信用风险、市场风险、操作风险的评估方法,是在比较了现有的国际先进风险管理理念后,结合我国国情,设计出了基于贝叶斯网络方法的信用风险量化模型和操作风险量化模型及基于动态贝叶斯网络方法的信用风险预警量化模型和操作风险预警量化模型。
短句来源
     Direct calculation inference algorithm for discrete dynamic bayesian network
     离散动态贝叶斯网络的直接计算推理算法
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     Study on path planning of UCAV based on dynamic Bayesian network
     基于动态贝叶斯网络的无人机路径规划研究
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     A Dynamic Bayesian Network Algorithm Based on Semi-Supervised Active Learning
     一种基于半监督主动学习的动态贝叶斯网络算法
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     Multisensor Fusion Tracking Using Continuous Dynamic Bayesian Network
     连续动态贝叶斯网络实现多传感器融合跟踪
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  “dynamic bayesian network”译为未确定词的双语例句
     State prediction based on the dynamic Bayesian network
     基于动态贝叶斯网的状态预测
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     Chinese Proper Names Recognition Based on Dynamic Bayesian Network
     基于动态贝叶斯网的中文专有名词识别
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     Inference and Algorithm for te Dynamic Bayesian Network
     贝叶斯网络推理与算法
     After introducing the theory framework of Bayesian network, this paper carried out the following research: researching Bayesian network based on information geometry theory, improving the performance of naive Bayesian classification(NBC), retrieving information from semi-structured text using rules embedded dynamic Bayesian network, and hierarchical text classification model based on Bayesian network.
     本文在贝叶斯网络基础理论框架的基础上,主要研究了以下几个方面的内容:基于信息几何理论的贝叶斯网络研究、朴素贝叶斯分类器的提升、规则方法与贝叶斯网络结合文本信息抽取研究、层次贝叶斯网络文本分类器。
短句来源
     Fitness function based on expectation is presented to convert incomplete data to complete data utilizing current best dynamic Bayesian network of evolutionary process. Thus dynamic Bayesian networks can be learned by using two Bayesian networks,prior network and transition network,to reduce the computational complexity.
     文中设计了结合数学期望的适应度函数 ,该函数利用进化过程中的最好动态Bayesian网把不完备数据转换成完备数据 ,使动态Bayesian网的学习分解为两个Bayesian网 (初始网和转换网 )的学习 ,简化了学习的复杂度 .
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  相似匹配句对
     Bayesian Analysis of Dynamic Images
     Bayesian运动图像分析
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     Dynamic
     地方科技动态
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     DYNAMIC
     动态
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     Dynamic Poisson Models and Bayesian Forecasting
     动态Poisson模型及Bayes预测
短句来源
     Dynamic Bayesian Network and Its Application to Speaker Recognition
     动态贝叶斯网络及其在说话人识别中的应用
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  dynamic bayesian network
A dynamic Bayesian network (DBN) is a probabilistic network that models interdependent entities that change over time.
      
Our approach is contrasted with a Dynamic Bayesian network for a simple medical example.
      
The evolution of the poses of the multiple body parts are processed by a dynamic Bayesian network (DBN).
      
We presented a hierarchical dynamic Bayesian network for unsupervised classification of expression sequences.
      
We introduce a dynamic Bayesian network-style formulation using the likelihood estimates.
      
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Dynamic Bayesian networks are a representation for complex stochastic processes.How to learn structure of Dynamic Bayesian networks from data is a hot problem of research.An evolutionary algorithm is proposed.Fitness function based on expectation is presented to convert incomplete data to complete data utilizing current best dynamic Bayesian network of evolutionary process.Thus dynamic Bayesian networks can be learned by using two Bayesian networks,prior network and...

Dynamic Bayesian networks are a representation for complex stochastic processes.How to learn structure of Dynamic Bayesian networks from data is a hot problem of research.An evolutionary algorithm is proposed.Fitness function based on expectation is presented to convert incomplete data to complete data utilizing current best dynamic Bayesian network of evolutionary process.Thus dynamic Bayesian networks can be learned by using two Bayesian networks,prior network and transition network,to reduce the computational complexity.Encoding is given,and genetic operators are designed which provides guarantee of convergence.Experimental results not only show this algorithm can be effectively used to learn Dynamic Bayesian networks structure from incomplete data sequences,but also illustrate the role of hidden variables and the influence of genetic control parameters on learned model.

动态Bayesian网是复杂随机过程的图形表示形式 ,从数据中学习建造动态Bayesian网是目前的研究热点问题 .本文针对该问题提出了一种遗传算法 .文中设计了结合数学期望的适应度函数 ,该函数利用进化过程中的最好动态Bayesian网把不完备数据转换成完备数据 ,使动态Bayesian网的学习分解为两个Bayesian网 (初始网和转换网 )的学习 ,简化了学习的复杂度 .此外 ,文中给出了网络结构的编码方案 ,设计了相应的遗传算子 .模拟实验结果表明 ,该算法能有效地从不完备数据序列中学习动态Bayesian网 ,并且实验结果说明了隐藏变量的作用和遗传控制参数对结果模型的影响

Dynamic Bayesian networks(DBNs) are a powerful methodology for representing and computing with uncertain problem of stochastic processes. Actions of two above human are modeled by combining agent technology with Bayesian theory. An approach of decomposition and incorporation is developed to resolve that multi-agent system based on dynamic Bayesian networks is intractable for exact calculations. The approach improves ability of model representing. The mutual cause relationship can be represented...

Dynamic Bayesian networks(DBNs) are a powerful methodology for representing and computing with uncertain problem of stochastic processes. Actions of two above human are modeled by combining agent technology with Bayesian theory. An approach of decomposition and incorporation is developed to resolve that multi-agent system based on dynamic Bayesian networks is intractable for exact calculations. The approach improves ability of model representing. The mutual cause relationship can be represented by the models.

动态贝叶斯网络(Dynamic Bayesian Networks,DBNs)是对具有随机过程性质的不确定性问题进行建模和处理的一个有力工具。该文将Agents技术和DBNs相结合来对两个以上的人的行为进行建模。提出一种分解和合并的方法来解决两个以上的Agents构成的DBNs的模型表示在计算上的难以处理性,同时还提高了模型的表示能力,且能表示变量之间互为因果的关系。

Plan recognition is a prediction theory for identifying and determining the intentions or the attempts of the agents monitored through observation data. In this paper, a plan recognition based method is presented to predict the anomaly events and intensions of potential intruders to a computer system using the system call sequences as observation data. The method is established on a dynamic Bayesian network with parameter compensation and an algorithm is developed to update this network. The experimental...

Plan recognition is a prediction theory for identifying and determining the intentions or the attempts of the agents monitored through observation data. In this paper, a plan recognition based method is presented to predict the anomaly events and intensions of potential intruders to a computer system using the system call sequences as observation data. The method is established on a dynamic Bayesian network with parameter compensation and an algorithm is developed to update this network. The experimental results show that this method has a good accuracy in predicting the intrusion intensions from the system call sequences.

规划识别是一种根据观察数据识别和推断被观察对象目的或意图的预测理论 .在计算机系统入侵检测研究中 ,为了提前预测出异常事件的发生 ,提出了一种基于规划识别理论的入侵企图预测方法 .通过对主机上的系统调用序列为观察对象建立预测模型 ,提出了一种带参数补偿的贝叶斯网络动态更新算法 ,对观察对象的目的进行预测 .实验结果表明动态贝叶斯网络对预测系统调用序列中的异常入侵企图有较高的精度 .

 
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