Abstract Details
Activity Number:
|
211
|
Type:
|
Roundtables
|
Date/Time:
|
Monday, August 10, 2015 : 12:30 PM to 1:50 PM
|
Sponsor:
|
Section on Statistical Computing
|
Abstract #315329
|
|
Title:
|
Bayesian Computation for High-Dimensional Data Sets
|
Author(s):
|
Naveen Narisetty*
|
Companies:
|
University of Michigan
|
Keywords:
|
High Dimensional Data ;
Bayesian Computation ;
Variable Selection ;
MCMC ;
Gibbs Sampling
|
Abstract:
|
Bayesian methods for high-dimensional data are being studied increasingly, providing new insights and advantages associated with these methods. Outreach of Bayesian methods to large data sets such as gene expression would heavily depend on availability of efficient computational algorithms. In this roundtable, we will discuss some of the existing Bayesian methods and computational algorithms for high-dimensional data sets. More specifically, we will focus on the linear regression set-up and consider the problems of estimation and variable selection. Although the theoretical properties of Bayesian methods for high-dimensional data are of independent interest, the emphasis of this discussion will be on computational issues such as the speed and scalability of the algorithms.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2015 program
|
For program information, contact the JSM Registration Department or phone (888) 231-3473.
For Professional Development information, 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.
2015 JSM Online Program Home
ASA Meetings Department
732 North Washington Street, Alexandria, VA 22314
(703) 684-1221 • meetings@amstat.org
Copyright © American Statistical Association.