Abstract Details
Activity Number:
|
539
|
Type:
|
Invited
|
Date/Time:
|
Wednesday, August 6, 2014 : 2:00 PM to 3:50 PM
|
Sponsor:
|
Section on Physical and Engineering Sciences
|
Abstract #310604
|
View Presentation
|
Title:
|
Approximate Bayesian Computation for Kepler Data
|
Author(s):
|
Jessi Cisewski*+ and Megan Shabram and Eric B. Ford and Chad Schafer
|
Companies:
|
Carnegie Mellon and University of Florida and Penn State and Carnegie Mellon
|
Keywords:
|
approximate Bayesian computation ;
astrostatistics ;
likeihood-free ;
computational statistics ;
exoplanets ;
Kepler
|
Abstract:
|
Approximate Bayesian computation (ABC) provides a way to do inference on problems where a likelihood cannot be written down or is computationally intractable. Due to the many complexities of astronomical observations, ABC provides a nice framework for inference, but it must be used with care. Extra-solar planets, or exoplanets, are planets orbiting stars outside our solar system. I will motivate the appeal for ABC in the exoplanet setting, and discuss an application of ABC to eccentricity distributions of exoplanets using Kepler data.
|
Authors who are presenting talks have a * after their name.
Back to the full JSM 2014 program
|
2014 JSM Online Program Home
For information, contact jsm@amstat.org or phone (888) 231-3473.
If you have questions about the Professional Development 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.