The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Abstract Details
Activity Number:
|
406
|
Type:
|
Contributed
|
Date/Time:
|
Tuesday, August 2, 2011 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Bayesian Statistical Science
|
Abstract - #303199 |
Title:
|
Hierarchical Poisson/Gamma Random Field Model
|
Author(s):
|
Jian Kang*+ and Timothy D. Johnson and Thomas E. Nichols
|
Companies:
|
University of Michigan and University of Michigan and University of Warwick
|
Address:
|
, , ,
|
Keywords:
|
Spatial Point Processes ;
Random Intensity Measure ;
Classification Model ;
Nonparametric Bayes ;
Hierarchical Model
|
Abstract:
|
To jointly analyze multiple groups of spatial point patterns, we propose a non-parametric Bayesian modeling approach that extends the Poisson/Gamma random field model (Wolpert and Ickstadt, 1998). In particular, each group of point patterns is modeled as a Poisson point process driven by a random intensity that is a kernel convolution of a gamma random field. The group-level gamma random fields are linked and modeled as a realization of a common gamma random field shared by all the groups. We resort to a hybrid algorithm with adaptive reject sampling embedded in a Markov chain Monte Carlo algorithm for posterior inference. Also, our model can be used to build a classifier of group label given spatial point patterns based on the corresponding posterior predictive probability. We illustrate our models on simulated examples and two real applications.
|
The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
Back to the full JSM 2011 program
|
2011 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Continuing Education program, please contact the Education Department.