Spatial Homogeneity Learning via Nonparametric Bayesian Methods

发布时间:2024-02-29 点击次数:

标题:Spatial Homogeneity Learning via Nonparametric Bayesian Methods






  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.


  Dr. Hu is the assistant professor at the University of Texas Health Science Center at Houston. Dr. Hu’s research mainly focuses on Bayesian nonparametric methods, spatial and spatio-temporal statistics, point process, and causal inference. Dr. Hu has also worked on the analysis of clinical trials, spatial transcriptomic, regional economics, environmental science, educational measurements, and sports data. Now, Dr. Hu is the associate editor of Biometrics, Environmental and Ecological Statistics and Statistics and its interface and he also serves as the Chair of ASA statistics in sports section and the Program Chair of ISBA East Asia Chapter. Dr. Hu is an elected member of ISI.