Collaborative Research: Hierarchical Sparsity-Inducing Gaussian Process Models for Bayesian Inference on Large Spatiotemporal Datasets

合作研究：大型时空数据集贝叶斯推理的层次稀疏诱导高斯过程模型

• 批准号: 1513654
• 负责人: Sudipto Banerjee
• 金额: $ 24万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513654&HistoricalAwards=false
• 关键词: Collaborative; Research; Hierarchical; Sparsity; Inducing

With the increasing capabilities of geographical referencing and remote-sensing technologies such as Geographical Information Systems (GIS) and Global Positioning Systems (GPS) that can identify geographical coordinates with a simple hand-held device, scientists and researchers in a variety of disciplines today have unprecedented access to spatially-referenced data. From identifying spatial disparities in health standards to more precise weather predictions, GIS technology is used today in almost every sphere of human life with beneficial effects that can be far-reaching. Statistical modeling and analysis for spatial data constitute a key element in harnessing the scientific potential of GIS and related technologies. As the scientific community moves into a data-rich era, there is unprecedented opportunity to build an understanding about how environmental ecosystems function and how they will respond to changing environmental conditions. This research project will advance data modeling in disciplines as diverse as forestry, ecology, public and environmental health, meteorology, engineering, and the geosciences. It will help discover complex scientific relationships, which, in turn, will lead to better analysis and understanding of our environment and how our ecosystem is evolving.Analysts and researchers using GIS technology are increasingly faced with analyzing massive amounts of spatial data. With spatial and spatial-temporal data becoming increasingly high-dimensional -- both in terms of number of observed locations and the number of observations per location -- scientists are seeking to hypothesize extremely complex relationships. Not surprisingly, statistical models accounting for spatial associations have become an enormously active area of research over the last decade and, in particular, hierarchical models capturing variation at multiple scales have become extremely popular for spatial modeling. These, in turn, lead to rather complex models that are computationally expensive and unfeasible even for moderately sized data sets. This project recognizes the increased computational demands in statistical modeling of large high-dimensional spatial and spatial-temporal data and offers a model-based setup to tackle a wide variety of data analytic problems. The emphasis of this project is on rigorous and principled statistical methodology that can be implemented on standard computing platforms, thereby ensuring accessibility for a very wide group of researchers. The project outlines a suite of spatial models that easily scale to massive databases and have a broad range of applications. Theoretical and methodological innovations that enhance current methods will be presented, and their practical implications will be illustrated using freely distributed open-source statistical software products developed as a part of this project.

随着地理参考和遥感技术（如地理信息系统（GIS）和全球定位系统（GPS））的能力不断增强，可以用简单的手持设备识别地理坐标，今天各种学科的科学家和研究人员能够前所未有地访问空间参考数据。从确定卫生标准的空间差异到更精确的天气预报，地理信息系统技术如今几乎应用于人类生活的每一个领域，产生了深远的有益影响。空间数据的统计建模和分析是利用地理信息系统和相关技术的科学潜力的关键因素。随着科学界进入一个数据丰富的时代，建立对环境生态系统如何运作以及它们如何对不断变化的环境条件作出反应的理解出现了前所未有的机会。该研究项目将推进林业、生态学、公共和环境卫生、气象学、工程学和地球科学等学科的数据建模。它将有助于发现复杂的科学关系，进而有助于更好地分析和理解我们的环境以及我们的生态系统是如何进化的。使用GIS技术的分析人员和研究人员越来越多地面临分析大量空间数据的问题。随着空间和时空数据变得越来越高维——无论是从观测地点的数量还是从每个地点的观测数量来看——科学家们正在寻求对极其复杂的关系进行假设。毫不奇怪，在过去十年中，考虑空间关联的统计模型已经成为一个非常活跃的研究领域，特别是，在多个尺度上捕获变化的分层模型已经成为空间建模中非常流行的方法。这反过来又导致了相当复杂的模型，这些模型在计算上非常昂贵，即使对于中等规模的数据集也是不可行的。该项目认识到在大型高维空间和时空数据的统计建模中增加的计算需求，并提供了一个基于模型的设置来解决各种数据分析问题。这个项目的重点是严谨和有原则的统计方法，可以在标准计算平台上实现，从而确保广泛的研究人员群体的可访问性。该项目概述了一套空间模型，可以很容易地扩展到大型数据库，并具有广泛的应用范围。将介绍增强当前方法的理论和方法创新，并使用作为本项目一部分开发的免费分发的开源统计软件产品说明其实际含义。