报告题目：Spatial Homogeneity Learning via Nonparametric Bayesian Methods

报告时间：2024年3月6日 上午10:00

报告地点：教学科研楼311室

主办单位：beat365正版唯一网站app

报告人：胡冠宇

报告人简介：休斯敦德克萨斯州大学健康科学中心的助理教授，他的研究主要集中在贝叶斯非参数方法、空间和时空统计学、点过程和因果推断。此外，还从事临床试验、空间转录组学、区域经济学、环境科学、教育测量和体育数据的分析，他是Biometrics, Environmental and Ecological Statistics, Statistics and its interface的副主编，担任ASA体育统计部分的主席和ISBA东亚分会的项目主席，当选为ISI委员。

摘要：In this talk, I will introduce two novel nonparametric Bayesian methods for learning spatial homogeneity patterns. Our methods have the advantage of effectively capturing both locally spatially contiguous clusters and globally discontiguous clusters. Posterior inferences are performed with an efficient Markov chain Monte Carlo (MCMC) algorithm. Simulation studies show that the inferences are accurate, and the method is superior compared to a wide range of competing methods. Several applications will be presented to reveal interesting findings based on proposed methods.