Abstract Details
Activity Number:
|
421
|
Type:
|
Topic Contributed
|
Date/Time:
|
Tuesday, August 6, 2013 : 2:00 PM to 3:50 PM
|
Sponsor:
|
SSC
|
Abstract - #308883 |
Title:
|
MCMC Clustering and Its Convergence Issues
|
Author(s):
|
Namdar Homayounfar*+ and Masoud Asgharian and Vahid Partovi Nia
|
Companies:
|
and McGill University and École Polytechnique Montréal
|
Keywords:
|
Metropolis-Hasting algorithm ;
Bayesian clustering ;
Convergence ;
Gibbs sampling ;
Split-Merge algorithm
|
Abstract:
|
Bayesian clustering using MCMC sampling is a popular approach. When a Markov chain Monte Carlo method is applied, the Markov chain samples are used to approximate the posterior after the chain is converged. When the data grouping is the concern, the convergence must be checked over the allocation space. The convergence of a Markov chain is verified, often using a trace plot, or using other common quantitative criteria mostly designed for a continuous state space. However, data allocation is a very large unordered discrete space and therefore the common convergence criteria is nontrivial to apply. We monitor the convergence of a clustering chain by a convergence criterion devised for the data allocation space.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2013 program
|
2013 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.
The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.
Copyright © American Statistical Association.