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Activity Number: 496
Type: Topic Contributed
Date/Time: Thursday, August 2, 2007 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #309648
Title: Bayesian Nonparametric Modeling for Spatial Data Using Dirichlet Processes
Author(s): Michele Guindani*+
Companies: The University of Texas M.D. Anderson Cancer Center
Address: 1515 Holcombe Blvd Unit 447, Houston, TX, 77030,
Keywords: Gaussian process ; Bayesian Nonparametrics ; Nonstationary process ; spatial random effects
Abstract:

We provide an overview of recent Bayesian Nonparametric modeling for the analysis of spatial data. The nonparametric specifications that underlie this work are generalizations of Dirichlet process mixture models. We attempt to interrelate these various choices either as generalizations or suitable limits. We offer comparison among these specifications by means of simulation and real data examples.

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Revised September, 2007