独立噪声观测集结的一个大偏差结果
A Large Deviations Result for Aggregation of Independent Noisy Observations
摘要：
噪声观测的感知和汇总不应被视为单独的问题。集体估计的质量涉及通过增加传感器的数量而增加的感测质量与如果传感器的数量太大则通常降低的聚合质量之间的困难的折衷。我们研究了一种策略，最佳聚合的独立传感器的合奏系统容量受限。我们表明，在大容量的限制，较大规模的聚合总是优于较小规模的聚合在较高的噪声水平，而低于临界值的噪声，存在适度的规模聚合水平，在实现最佳估计。
英文摘要：
Sensing and aggregation of noisy observations should not be considered as separate issues. The quality of collective estimation involves a difficult tradeoff between sensing quality which increases by increasing the number of sensors, and aggregation quality which typically decreases if the number of sensors is too large. We examine a strategy for optimal aggregation for an ensemble of independent sensors with constrained system capacity. We show that in the large capacity limit larger scale aggregation always outperforms smaller scale aggregation at higher noise levels, while below a critical value of noise, there exist moderate scale aggregation levels at which optimal estimation is realized.
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