Abstract Details
Activity Number:
|
61
|
Type:
|
Topic Contributed
|
Date/Time:
|
Sunday, August 4, 2013 : 4:00 PM to 5:50 PM
|
Sponsor:
|
Section on Bayesian Statistical Science
|
Abstract - #308108 |
Title:
|
Multivariate Bayesian Convex Regression
|
Author(s):
|
Lauren Hannah*+ and David B. Dunson
|
Companies:
|
Columbia University and Duke University
|
Keywords:
|
Convex Regression ;
Bayesian Methods ;
Nonparametric
|
Abstract:
|
Convex optimization solution methods only work when objective functions and constraints are fully known. In many settings like reinforcement learning and approximate dynamic programming, these need to be estimated from data. The estimates need to maintain convexity so they can be used in conjunction with a solver. We propose a new Bayesian nonparametric multivariate method for regression subject to convexity constraints. It characterizes the unknown regression function as the max of a random collection of unknown hyperplanes. The Bayesian method offers multiple benefits over frequentist alternatives: model averaging produces estimates that are stable in an optimization setting, posterior sampling can be used to approximate distributional constraints for robust optimization, and the modularity of Bayesian methods easily extends this model to partially shape constrained settings.
|
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.
Copyright © American Statistical Association.