Abstract Details
Activity Number:
|
463
|
Type:
|
Invited
|
Date/Time:
|
Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Bayesian Statistical Science
|
Abstract #314319
|
|
Title:
|
An Unbiased and Scalable Monte Carlo Method for Bayesian Inference for Big Data
|
Author(s):
|
Murray Pollock* and Paul Fearnhead and Adam Michael Johansen and Gareth O. Roberts
|
Companies:
|
University of Warwick and Lancaster University and University of Warwick and University of Warwick
|
Keywords:
|
Exact simulation ;
Langevin diffusion ;
Quasi-stationarity ;
Big data
|
Abstract:
|
This talk will introduce a new methodology for exploring posterior distributions by modifying methodology for exactly (without error) simulating diffusion sample paths. This new method has remarkably good scalability properties as the size of the data set increases (it has sub-linear cost, and potentially no cost), and therefore is a natural candidate for "Big Data" inference.
|
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.