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Parallel inference for massive distributed spatial data using low-rank models

机译：使用低秩模型对大量分布式空间数据进行并行推理

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摘要

Due to rapid data growth, statistical analysis of massive datasets often has to be carried out in a distributed fashion, either because several datasets stored in separate physical locations are all relevant to a given problem, or simply to achieve faster (parallel) computation through a divide-and-conquer scheme. In both cases, the challenge is to obtain valid inference that does not require processing all data at a single central computing node. We show that for a very widely used class of spatial low-rank models, which can be written as a linear combination of spatial basis functions plus a fine-scale-variation component, parallel spatial inference and prediction for massive distributed data can be carried out exactly, meaning that the results are the same as for a traditional, non-distributed analysis. The communication cost of our distributed algorithms does not depend on the number of data points. After extending our results to the spatio-temporal case, we illustrate our methodology by carrying out distributed spatio-temporal particle filtering inference on total precipitable water measured by three different satellite sensor systems.
机译：由于数据的快速增长，海量数据集的统计分析通常必须以分布式方式进行，要么是因为存储在单独物理位置中的多个数据集都与给定问题相关，要么只是为了通过数据采集实现更快（并行）的计算。分而治之方案。在这两种情况下，挑战在于获得不需要在单个中央计算节点上处理所有数据的有效推断。我们表明，对于使用非常广泛的一类空间低秩模型，可以将其写为空间基函数与精细尺度变化分量的线性组合，可以对大量分布式数据进行并行空间推断和预测准确地表示结果与传统的非分布式分析相同。我们的分布式算法的通信成本不取决于数据点的数量。将结果扩展到时空情况后，我们通过对由三个不同的卫星传感器系统测量的总可沉淀水量进行分布式时空粒子滤波推断，来说明我们的方法。

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