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bayesian reasoning
相关语句
  贝叶斯推理
     The effect of informational external representation on Bayesian reasoning was studied from the perspective of distributed cognition.
     基于分布式认知观点,考察信息的外部表征方式对人们解决贝叶斯推理问题的影响。
短句来源
     the output is a Bayesian reasoning process with the fusion of multi pieces of information;
     其输出是多信息合成的贝叶斯推理过程;
短句来源
     Bayesian tasks were used in the present study to explore the influence of subject relevancy on Bayesian reasoning.
     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。
短句来源
     REVIEW ON BAYESIAN REASONING RESEARCH
     贝叶斯推理研究综述
短句来源
     The Effect of Informational External Representation on Bayesian Reasoning
     信息的外部表征方式对贝叶斯推理成绩的影响
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  bayesian推理
     Application of Bayesian reasoning to a remote query answering expert system
     Bayesian推理在远程答疑专家系统中的应用
短句来源
     The system architecture and principle of a remote query answering expert system(RQAES) based on Bayesian reasoning was introduced. The Bayesian method of reasoning and decision making was analyzed, and the design of a knowledge base was presented.
     介绍了基于Bayesian推理的远程答疑专家系统RQAES的系统结构及其工作原理 ,重点分析了Bayesian推理与决策的方法及知识表示与知识库设计技术。
短句来源
  bayesian推理的
     The system architecture and principle of a remote query answering expert system(RQAES) based on Bayesian reasoning was introduced. The Bayesian method of reasoning and decision making was analyzed, and the design of a knowledge base was presented.
     介绍了基于Bayesian推理的远程答疑专家系统RQAES的系统结构及其工作原理 ,重点分析了Bayesian推理与决策的方法及知识表示与知识库设计技术。
短句来源
  “bayesian reasoning”译为未确定词的双语例句
     Facilitating Effect of Transparent Nested-Sets Relations on Bayesian Reasoning
     明确嵌套集合关系对贝叶斯推理的促进效应
短句来源
     A Study on the Relationship Between Knowledge Schema and Bayesian Reasoning
     知识图式对贝叶斯推理的影响
短句来源
     After categorizing all the security related system properties into four models, which are represented by their corresponding fuzzy membership functions, the real-time probabilities of whether the specific security properties are abnormal or not will be calculated as according to the fuzzy membership functions of the models they belong to, and a decision whether the supervised system is in a abnormal state is thus made from the synthesizing of all these probabilities by the Bayesian reasoning network.
     该模型结合了模糊概率赋值与贝叶斯置信网络推理方法,通过将系统中与安全相关的特征属性进行分类,并以模糊隶属度函数的形式给出其取特定值时系统行为异常与否的置信度; 贝叶斯置信网络在综合这些实时模糊概率值的基础上,对系统是否正遭受入侵做出决策。
短句来源
     In this paper, two experiments were designed to examine the Bayesian reasoning's performance features and effective factors of high school students who were in Grade One.
     本文设计了两个实验,考察高一学生贝叶斯推理的完成特点及影响因素。
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  bayesian reasoning
We then report two experiments designed to test whether individuals follow Bayesian reasoning and whether groups are able to overcome biased individual information processing.
      
After that, these predicted errors are diagnosed and recovered using Bayesian reasoning and genetic algorithms.
      
I re-examine Coherence Arguments (Dutch Book Arguments, No Arbitrage Arguments) for diachronic constraints on Bayesian reasoning.
      
On the contrary, Bayesian reasoning presupposes knowledge that cannot itself be obtained by Bayesian reasoning, making generic Bayesianism an incoherent theory of belief updating.
      
It will take into account also examples of mistakes in Bayesian reasoning and mistakes of reasoning committed by clinicians regard inductive reasoning.
      
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A Bayesian Gaussian neural network is put forward for nonlinear system identification. This network has the following features: the topology, connection weights and thresholds can be set immediately when training samples are attainable; the output is a Bayesian reasoning process with the fusion of multi pieces of information; while the training process is only to optimize the input factors, which makes the training simpler than that of back propagation neural network. The self organizing ability...

A Bayesian Gaussian neural network is put forward for nonlinear system identification. This network has the following features: the topology, connection weights and thresholds can be set immediately when training samples are attainable; the output is a Bayesian reasoning process with the fusion of multi pieces of information; while the training process is only to optimize the input factors, which makes the training simpler than that of back propagation neural network. The self organizing ability of this network can also be easily achieved in an optimal way, so that when the dynamics of the system drifts, the network is able to swiftly optimize its connection weights and thresholds according to new training samples, which is very important for on line adaptation of network to systems. In applications, the working of this network can be an alternating process. Simulation shows that this network is comparable to the back propagation neural network in the prediction of a single input single output nonlinear system, and its self organizing ability outperforms the latter which has fixed connection weights and thresholds.

提出了一种贝叶斯-高斯神经网络用于非线性系统辨识。网络的拓扑结构和连接权值可以由训练样本直接得到;其输出是多信息合成的贝叶斯推理过程;其训练过程仅是用于学习输入因子,从而使训练速度远高于一般的反向传播神经网络。此外,网络的自组织过程根据某种最优准则实现,使得当系统动态特性发生漂移时,网络可以根据新的样本迅速优化其连接权值,而不需要重新进行学习,这对于实时应用是十分重要的。实际应用中,网络的工作过程可以是推广与自组织交替进行的过程。仿真研究表明,该网络的辨识效果可与经拓扑结构优选的反向传播网络相比,而其自组织能力则是权值不变的后者无法相比的。

The developments and problems of evidence reasoning are discussed in this article. Evidence reasoning is com-pared with Bayesian reasoning,fuzzy logic,rule-based reasoning and random set theory. Improvement of evidence reasoning andits extension to probability and fuzzy set are introduced It is found that evidence theory has many applications Finally, its direc-tion of further development is proposed.

综合论述了证据推理的进展和存在的问题,把证据推理与贝叶斯推理、模糊逻辑推理、基于规则的推理。随机集理论进行了横向比较,讨论了其间的必然的联系和为证据推理的发展提供的基础,随后总结了证据推理的改进及其在概率范围和模糊集等方面推广的进展,最后提出了证据推理的广泛应用及其研究方向.

The influence diagram is introduced to maneuvering decision making in one-to-one air combat for the first time. In the influence diagram model, the possible combat situations related to each maneuver alternative are associated with a probability and a utility. The effects of sensor information that will reduce the uncertainty of the model are evaluated with Bayesian reasoning. Influence diagram analysis produces a probability distribution of the overall utility that represents the successfulness of a maneuver....

The influence diagram is introduced to maneuvering decision making in one-to-one air combat for the first time. In the influence diagram model, the possible combat situations related to each maneuver alternative are associated with a probability and a utility. The effects of sensor information that will reduce the uncertainty of the model are evaluated with Bayesian reasoning. Influence diagram analysis produces a probability distribution of the overall utility that represents the successfulness of a maneuver. The final decision is based on the decision criterion. At last, the impacts of different factors on the outcome of the maneuvering decision are determined by sensitivity analysis.

首次将影响图分析方法引入到一对一空战机动决策中。在影响图模型中 ,各种空战态势下的每个备选机动方案都与一个概率值和一个效用值相联系 ,根据贝叶斯定理可以利用传感器信息降低结果的不确定性 ,影响图分析为综合效用值提供了概率分布 ,表示完成各备选动作的成功率 ,最终的决策根据所选的决策标准而定。最后进行灵敏度分析以决定各因素对决策结果的影响程度。

 
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