Conference on Advances in Data Science: Talk Titles & Abstracts for Invited Speakers

Guanyu Hu, University of Missouri

Title: Bayesian Nonparametric Learning for Spatial Point Process

Abstract: 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 earquake occurrences and basketball shot charts are presented as illustrations of our proposed methods.