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A Method for Measuring the Incremental Information Contributed from Non-Stationary Spatio-Temporal Data to be Fused

机译:一种测量非融合时空数据贡献的增量信息的方法

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摘要

Complex hydrologic problems, such as estimating stream discharge (flow), require the utilization (fusion) of multiple sets of measurements (features). Due to the computational cost of incorporating all available features, it is desirable to use a reduced set of the most informative ones. A method is presented for determining the information gain of different feature subsets in a Bayesian network. The method is applied to the problem of estimating flow in a Spatio-Temporal Bayesian Network (STBN), under the constraint that the features retain their original physical meaning.
机译:复杂的水文问题,例如估算河流流量(流量),需要利用(融合)多组测量值(特征)。由于合并所有可用功能的计算成本,因此希望使用减少的一组最有用的功能。提出了一种用于确定贝叶斯网络中不同特征子集的信息增益的方法。该方法适用于时空贝叶斯网络(STBN)中的流量估计问题,其特征是保留这些特征的原始物理意义。

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