On Dynamic Nearest-Neighbor Gaussian Process Models for High-Dimensional Spatiotemporal Data

来源 :上海交通大学 | 被引量 : 0次 | 上传用户：drjcs

【摘要】

：

【作者】

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Sudipto Banerjee Abhirup Datta Andrew O.Finley

【机构】

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Department of Biostatistics,University of California Los Angeles,California 90095-1772,USA

【出处】

：

上海交通大学

【发表日期】

：

2016年4期

【关键词】

：

Bayesian inference Gaussian processes Hierarchical models Scalable processes Spa

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　　With the growing capabilities of Geographic Information Systems(GIS)and user-friendly software,statisticians today routinely encounter geographically referenced data containing observations from a large number of spatial locations and time points.Over the last decade,hierarchical spatial-temporal process models have become widely deployed statistical tools for researchers to better understanding the complex nature of spatial and temporal variability.

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On Dynamic Nearest-Neighbor Gaussian Process Models for High-Dimensional Spatiotemporal Data

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