Mathematical Sciences: Multivariate Bayesian Classification Methods in Spatial Statistics

主要研究者将探索使用 空间多变量贝叶斯分类方法 统计 他计划研究 遥感数据使用迭代贝叶斯和经验 贝叶斯程序。 他计划将现有的模型扩展到 更广泛的背景下，并希望改善目前的模式，在各种 的方式 模拟和实际数据都将进行研究。 他将 研究块金效应模型，各种空间相关性 结构和自适应起始值模型， 共轭先验族（同时使用迭代程序 后分类）。 结果应适用于 农业和地球物理方面的卫星图像， 图像重建和模式识别中的问题。

The principal investigator will explore the use of multivariate Bayesian classification methods in spatial statistics. He plans to study the contextual classification of remotely sensed data using iterative Bayesian and empirical Bayesian procedures. He plans to extend existing models to broader contexts,and hopes to improve current models in various ways. Both simulated and actual data will be studied. He will study nugget effect models, various spatial correlation structures, and adaptive starting value models using natural conjugate prior families (while using iterative procedures for posterior classification). Results should be applicable to satellite imagery in agricultural and geophysical contexts, to image reconstruction, and to problems in pattern recognition.