U.S.-Brazil Cooperative Research: Multivariate Bayesian Classification Methods in Spatial Statistics

美国-巴西合作研究：空间统计中的多元贝叶斯分类方法

• 批准号: 9016175
• 负责人: S. James Press
• 金额: $ 1.01万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1991
• 资助国家: 美国
• 起止时间: 1991-08-01 至 1993-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9016175&HistoricalAwards=false
• 关键词: U.S.; Brazil; Cooperative; Research; Multivariate

This award will support collaborative research by Professor James Press of the University of California at Riverside and Professor Ruben Klein of the National Laboratory for Scientific Computation (LNCC) in Rio de Janeiro, Brazil. The aim of the research is to study multivariate Bayesian classification methods in spatial statistics. Image reconstruction and the contextual classification of remotely sensed data using iterative Bayesian and empirical Bayesian procedures will add an international dimension to this collaboration. The researchers, who have coauthored a number of journal articles on this topic, plan to extend existing models to broader contexts and to improve current models in various ways. Both simulated and actual data will be studied. They propose to study "nugget effect" models, various spatial correlation structures, and adaptive starting value models using natural conjugate prior families (while using iterative procedures for predictive classification). Results should be applicable to satellite imagery in agricultural and geophysical contexts, image reconstruction, and problems in pattern recognition.

该奖项将支持加州大学河滨分校的James Press教授和巴西里约热内卢国家科学计算实验室(LNCC)的Ruben Klein教授的合作研究。本研究旨在研究空间统计学中的多元贝叶斯分类方法。利用迭代贝叶斯和经验贝叶斯程序对遥感数据进行图像重建和背景分类，将为这一合作增加国际层面。研究人员已经就这一主题共同撰写了许多期刊文章，他们计划将现有模型扩展到更广泛的背景下，并以各种方式改进现有模型。将对模拟数据和实际数据进行研究。他们建议研究“块金效应”模型、各种空间相关性结构，以及使用自然共轭先验族的自适应起始值模型(同时使用迭代程序进行预测分类)。结果应适用于农业和地球物理背景下的卫星图像、图像重建和模式识别问题。