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A method for identifying a subset of components of a system

机译:一种识别系统组件子集的方法

摘要

A method of identifying a subset of components of a system based on data obtained from the system using at least one training sample from the system is disclosed. The method comprises obtaining a linear combination of components of the system and weightings of the linear combination of components. The weightings have values based on data obtained from the at least one training sample. The at least one training sample has a known feature. A model of a probability distribution of the known feature is obtained. The model is conditional on the linear combination of components. A prior distribution for the weighting of the linear combination of the components is obtained. The prior distribution comprises a hyperprior having a high probability density close to zero. The hyperprior is such that it is not a Jeffrey's hyperprior. The prior distribution and the model are combined to generate a posterior distribution and the subset of components based on a set of the weightings that maximise the posterior distribution is identified.
机译:公开了一种使用来自系统的至少一个训练样本基于从系统获得的数据来识别系统的组件的子集的方法。该方法包括获得系统的组件的线性组合和组件的线性组合的权重。加权具有基于从至少一个训练样本获得的数据的值。至少一个训练样本具有已知特征。获得已知特征的概率分布的模型。该模型以组件的线性组合为条件。获得分量线性组合的加权的先验分布。先验分布包括具有接近零的高概率密度的超优先级。超级优先级不是杰弗里的超级优先级。将先验分布和模型组合以生成后验分布,并基于最大化后验分布的一组权重来标识组件的子集。

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