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

机译:用于测量从非静止时空数据融合的增量信息的方法

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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,例如估计流放电(流量),需要多组测量(特征)的利用(融合)。由于包含所有可用特征的计算成本,期望使用减少的最佳信息。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。该方法应用于估计时空贝叶斯网络(STBN)中的流量的问题,在该特征保留其原始物理含义的约束下。

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