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This paper addresses issues in constructing a Bayesian network domain model for diagnostic purposes from expert knowledge.


TailoredtoFit Bayesian Network Modeling of Expert Diagnostic Knowledge


The object class is generatively modeled using a simple Bayesian network with a central hidden node containing location and scale information, and nodes describing object parts.


Our algorithm is implemented in a commercial Bayesian Network software package, AgenaRisk, which allows model construction and testing to be carried out easily.


In particular, to determine Jeffrey's prior for this model family, we show how to compute the (expected) Fisher information matrix for a fixed but arbitrary Bayesian network structure.

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 In order to solve the uncertainty problems in engineering diagnosis, a threelayered topological model using Bayesian diagnostic network was proposed based on condition, operation, fault, and symptom. Moreover, it used the superiority of Bayesian network in dealing with uncertainty and experts synthetically experience. The model represented the complicated relationships in engineering diagnosis intuitively. A practical example shows that its evidences for diagnosis are more sufficient and its... In order to solve the uncertainty problems in engineering diagnosis, a threelayered topological model using Bayesian diagnostic network was proposed based on condition, operation, fault, and symptom. Moreover, it used the superiority of Bayesian network in dealing with uncertainty and experts synthetically experience. The model represented the complicated relationships in engineering diagnosis intuitively. A practical example shows that its evidences for diagnosis are more sufficient and its result of network inference is more consistent with practice in comparison with naive Bayesian networks, because it takes account of operating actual conditions and operation records of equipment. This model has been used successfully for diagnosis of an industrial gas turbine in a refinery of a petrochemical complex.  为了解决工程诊断中的不确定性,提出了一种基于工况操作故障征兆的3层拓扑结构的贝叶斯诊断网络模型.该模型发挥了贝叶斯网络解决不确定性问题的优越性,融合了专家的经验知识,用图形化方式直观地表达了诊断中的复杂关系.应用实例表明,与传统质朴型贝叶斯诊断网络相比,该模型考虑了诊断对象的实际运行工况和操作情况,用于诊断的证据信息更充分,网络的诊断推理结果更符合诊断实际,已成功地应用于某石化公司炼油厂的烟机诊断中.  Bayesian network is a new inference and express method of uncertain knowledge. It is proposed an inference and express technique for remote sensing imagery data which has complexity and uncertainty based on Bayesian Network Model(BNM). In the paper, the LU data, TSP and LST/Albedo data of AVHRR timesequence imagery which get from the project of ChinaJapan Asian dust storm in 2002 are used to analyze the dust storm and at the same time BNM is used to describe the knowledge and information inference.... Bayesian network is a new inference and express method of uncertain knowledge. It is proposed an inference and express technique for remote sensing imagery data which has complexity and uncertainty based on Bayesian Network Model(BNM). In the paper, the LU data, TSP and LST/Albedo data of AVHRR timesequence imagery which get from the project of ChinaJapan Asian dust storm in 2002 are used to analyze the dust storm and at the same time BNM is used to describe the knowledge and information inference. The satisfied results are given in the paper with the method.  贝叶斯网络是一种不确定性知识的推理和描述技术,针对遥感数据的复杂性和不确定性,该文提出了一种基于贝叶斯网络模型的遥感数据推理和描述技术。文中利用 2002年春季中日亚洲沙尘暴项目的土地利用数据(LU),沙尘监测数据(TSP),卫星 AVHRR时间序列 LST/Albedo数据,采用贝叶斯网络模型进行了知识描述和信息推理预测实验,取得了较好的效果。  In this paper, the technical procedures and data analysis in using Bayesian network to process airborne data are described. The result shows that the Bayesian network method has three advantages. First, both the prior probability and features are used to establish the probability estimation weighing relations shown in associated probability chart; Second, the linkage of the directed acyclic graph (DAG) and classes can clearly show the relations between independence vectors (bands) and classes; Third,... In this paper, the technical procedures and data analysis in using Bayesian network to process airborne data are described. The result shows that the Bayesian network method has three advantages. First, both the prior probability and features are used to establish the probability estimation weighing relations shown in associated probability chart; Second, the linkage of the directed acyclic graph (DAG) and classes can clearly show the relations between independence vectors (bands) and classes; Third, according to the contribution degree of three inputted bands quantitatively shown in associated probabilities for each class, the prior probability can be revised. The study results suggest that Bayesian network is likely to become a new practical method for remote sensing data processing.  介绍了利用贝叶斯网络对航空遥感数据进行分类的算法和过程,认为贝叶斯网络具有以下优点:充分利用和综合了先验知识与样本信息;采用有向无环图(DAG)的方式描述了多特征数据间的相互关系;给出了联合概率表,并通过联合概率表给出了每个像元属于不同类别的概率。研究结果表明,贝叶斯网络可以为遥感数据分类提供一种新方法。   << 更多相关文摘 
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