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Activity Number: 601
Type: Contributed
Date/Time: Wednesday, August 12, 2015 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #317434
Title: Nonparametric Bayesian Model for Spatial Point Processes
Author(s): Gavino Puggioni* and Lance Waller and Luca Gerardo-Giorda and Leslie Real
Companies: University of Rhode Island and Emory University and Basque Center for Applied Mathematics and Emory University
Keywords: Point Processes ; Dirichlet Process
Abstract:

In this paper we propose a dynamic Bayesian non parametric approach for spatiotemporal point processes. The proposed model involves a dynamic density estimation problem, with the specification of a prior based on a Dirichlet process mixture of bivariate normal distributions at each point in time. Temporal dependence is introduced through the atoms that evolve as dynamic linear models. Comparison with other existing methods and application to real data are provided.

Authors who are presenting talks have a * after their name.

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