Academic

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.