JSM 2013 Home
Online Program Home
My Program

Abstract Details

Activity Number: 48
Type: Invited
Date/Time: Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
Sponsor: IMS
Abstract - #307022
Title: Ensemble Learning for Big Data
Author(s): Hugh A. Chipman*+ and Robert E. McCulloch and Matthew Pratola and Dave Higdon and James Gattiker and Steven L Scott
Companies: Acadia University and The University of Chicago Booth School of Business and Simon Fraser University and Los Alamos National Laboratories and Los Alamos National Laboratories and Google
Keywords: supervised learning ; Bayesian methods ; tree model ; ensemble ; big data ; high-performance computing
Abstract:

Ensembles are powerful supervised learning methods that combine together many individual models in an adaptive manner. Bayesian Additive Regression Trees (BART) are a successful statistical version of ensemble models, offering full inference. However, computation can be expensive for big data, especially for MCMC. We discuss two different approaches to parallelizing BART, either via MPI or via a novel implementation that runs parallel MCMC runs on independent machines.


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.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.