报告题目:Bayesian Nonparametric Learning for Spatial Point Process

报告时间：2023.3.14上午10:00 – 12:00

报告地点：数理楼142

摘要:

Spatial point pattern data are routinely encountered. A flexible regression model for the underlying intensity is essential to characterizing the spatial point pattern. We present novel nonparametric Bayesian methods for learning the underlying intensity surface built upon nonparametric Bayesian methods. Our method has the advantage of effectively encouraging local spatial homogeneity when estimating a globally heterogeneous intensity surface. 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 such as earthquake occurrences and basketball shot charts are presented as illustrations of our proposed methods.

报告人：胡冠宇，University of Missouri统计系助理教授，主要研究领域为空间统计学、贝叶斯非参数方法、生存分析等，主持两项NSF科研项目，在统计学、地球科学等领域的国际学术期刊发表论文十余篇。