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: 1513481
• 负责人: Andrew Finley
• 金额: $ 8万
• 依托单位: Michigan State University
• 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 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1513481&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.

随着地理参照和遥感技术的能力不断提高，例如地理信息系统和全球定位系统，可以用一个简单的手持装置确定地理坐标，今天，各种学科的科学家和研究人员都有前所未有的机会获得空间参照数据。 从确定健康标准的空间差异到更精确的天气预测，GIS技术今天几乎应用于人类生活的各个领域，其有益影响可能是深远的。 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一个关键因素。 随着科学界进入一个数据丰富的时代，有前所未有的机会来了解环境生态系统如何运作以及它们将如何应对不断变化的环境条件。 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，从而更好地分析和理解我们的环境以及生态系统的演变。使用GIS技术的分析人员和研究人员越来越多地面临着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（无论是在观察位置的数量还是每个位置的观察数量方面），科学家们正在寻求假设极其复杂的关系。 毫不奇怪，统计模型占空间协会已成为一个非常活跃的研究领域在过去的十年中，特别是，分层模型捕捉变化在多个尺度上已成为非常流行的空间建模。 这些反过来又会导致相当复杂的模型，即使对于中等大小的数据集，这些模型在计算上也是昂贵的和不可行的。 该项目认识到大型高维空间和时空数据统计建模的计算需求增加，并提供了一个基于模型的设置来解决各种数据分析问题。 该项目的重点是严格和原则性的统计方法，可以在标准计算平台上实施，从而确保广泛的研究人员群体可以使用。 该项目概述了一套空间模型，可以轻松扩展到大型数据库，并具有广泛的应用。 将介绍加强现有方法的理论和方法创新，并将使用作为本项目一部分开发的免费分发的开放源码统计软件产品说明其实际影响。