Space and Space-Time Models for Large Datasets

大型数据集的空间和时空模型

• 批准号: 0906765
• 负责人: Bruno Sanso
• 金额: $ 17.5万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0906765&HistoricalAwards=false
• 关键词: Space; Time; Models; Large; Datasets

In this project the investigator focuses on linear representations ofGaussian random fields. He considers models that either avoid explicitcomputation of the covariance matrix or do so for relatively smalldimensions. The correlation functions of the considered processes arevery general, avoiding particular symmetries or stationarity. The PIconsiders models that work in domains of general dimension and, inparticular, on the sphere, for gridded and non-gridded data. The PI useshierarchical methods of inference to account for measurement errors anddifferent sources of information. He develops statistically rigorousprocedures to summarize the information of dynamically evolving randomfields in a reduced number of time series and designs fast Monte Carlomethods that take advantage of parallel architectures.The investigator studies statistical models for spatial andspatio-temporal processes observed at a large number of locations andtime steps. The PI's research addresses the need for increasinglysophisticated models that can deal with different sources ofinformation, include expert opinion and handle effectively severalsources of uncertainty. While operating on large datasets, the PI'smodels consider time evolving dynamics and spatial heterogeneity.This allows for the analysis of phenomena on global scales, making gooduse of state of the art inferential and computational methods. Anexample of a problem where unified inferences from a variety of datasources is needed is the prediction of future climate from differentclimate models. Climate change prediction currently has a large societalimpact. This research provides tools to enhance our quantitativeunderstanding of the uncertainties involved in such predictions. Thiswill improve the ability of decision makers to make quality policydecisions.

在这个项目中，研究者专注于高斯随机场的线性表示。他考虑的模型要么避免协方差矩阵的显式计算，要么在相对较小的维度上这样做。所考虑的过程的相关函数非常一般，避免了特定的对称性或平稳性。pic考虑在一般维度域中工作的模型，特别是在球体上，用于网格和非网格数据。PI使用分层推理方法来解释测量误差和不同的信息来源。他开发了统计上严格的程序，以减少时间序列的数量来总结动态发展的随机场的信息，并设计了利用并行架构的快速蒙特卡罗方法。研究者研究了在大量地点和时间步长观察到的空间和时空过程的统计模型。PI的研究解决了对日益复杂的模型的需求，这些模型可以处理不同来源的信息，包括专家意见，并有效地处理几个不确定性来源。在处理大型数据集时，PI模型考虑了时间演化动力学和空间异质性。这允许在全球尺度上分析现象，充分利用最先进的推理和计算方法。需要从各种数据源进行统一推断的一个问题是，从不同的气候模式预测未来气候。气候变化预测目前具有很大的社会影响。这项研究提供了工具，以提高我们对这种预测所涉及的不确定性的定量理解。这将提高决策者做出质量决策的能力。