Translator Disclaimer
June 2012 Mixture Modeling for Marked Poisson Processes
Matthew A. Taddy, Athanasios Kottas
Bayesian Anal. 7(2): 335-362 (June 2012). DOI: 10.1214/12-BA711

Abstract

We propose a general inference framework for marked Poisson processes observed over time or space. Our modeling approach exploits the connection of nonhomogeneous Poisson process intensity with a density function. Nonparametric Dirichlet process mixtures for this density, combined with nonparametric or semiparametric modeling for the mark distribution, yield flexible prior models for the marked Poisson process. In particular, we focus on fully nonparametric model formulations that build the mark density and intensity function from a joint nonparametric mixture, and provide guidelines for straightforward application of these techniques. A key feature of such models is that they can yield flexible inference about the conditional distribution for multivariate marks without requiring specification of a complicated dependence scheme. We address issues relating to choice of the Dirichlet process mixture kernels, and develop methods for prior specification and posterior simulation for full inference about functionals of the marked Poisson process. Moreover, we discuss a method for model checking that can be used to assess and compare goodness of fit of different model specifications under the proposed framework. The methodology is illustrated with simulated and real data sets.

Citation

Download Citation

Matthew A. Taddy. Athanasios Kottas. "Mixture Modeling for Marked Poisson Processes." Bayesian Anal. 7 (2) 335 - 362, June 2012. https://doi.org/10.1214/12-BA711

Information

Published: June 2012
First available in Project Euclid: 16 June 2012

zbMATH: 1330.62200
MathSciNet: MR2934954
Digital Object Identifier: 10.1214/12-BA711

Rights: Copyright © 2012 International Society for Bayesian Analysis

JOURNAL ARTICLE
28 PAGES


SHARE
Vol.7 • No. 2 • June 2012
Back to Top