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

该奖项将支持教授的合作研究 位于滨江的加州大学的詹姆斯·普雷斯和 美国国家科学实验室的鲁本·克莱因教授 计算（LNCC）在里约热内卢，巴西. 的目的 研究是研究多元贝叶斯分类 空间统计方法。 图像重建和上下文分类 遥感数据使用迭代贝叶斯和经验 贝叶斯程序将为此增加一个国际层面 协作 研究人员共同撰写了一些 关于这一主题的期刊文章，计划扩大现有的 模型，以更广泛的背景下，并改善目前的模式， 各种方式 模拟和实际数据都将进行研究。 他们建议研究“块金效应”模型， 相关结构和自适应起始值模型 使用自然共轭先验族（同时使用迭代 预测分类的程序）。 结果应 适用于农业和地球物理方面的卫星图像 背景、图像重建和模式中的问题 识别.

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.