This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
|
5
|
Type:
|
Invited
|
Date/Time:
|
Sunday, August 1, 2010 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Bayesian Statistical Science
|
Abstract - #305997 |
Title:
|
Cloud Computing for Bayesian Hidden Markov Models
|
Author(s):
|
Steven Lee Scott*+
|
Companies:
|
Google
|
Address:
|
1600 Amphitheatre Parkway, Mountain View, CA, 94043,
|
Keywords:
|
Markov chain Monte Carlo ;
cloud computing ;
parallel computation
|
Abstract:
|
Hidden Markov models (HMM's) are a useful way to model streams of data produced by groups of subjects. Bayesian inference for HMM's typically involves data augmentation implemented using a forward-backward sampling procedure. The output of each iteration is a set of complete data sufficient statistics that are used in a draw of model parameters. With large data set the data augmentation step is many orders of magnitude more expensive than the parameter draw, and so is a natural candidate for parallelization. This talk describes a parallel implementation of a Bayesian HMM in a "cloud" with many thousands of processors. We describe the gains in speed experienced when computing with very large data sets, as well as the software design and programming decisions faced when computing in a very large cluster.
|
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 2010 program
|
2010 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.