|
Activity Number:
|
331
|
|
Type:
|
Roundtables
|
|
Date/Time:
|
Tuesday, August 4, 2009 : 12:30 PM to 1:50 PM
|
|
Sponsor:
|
Section on Bayesian Statistical Science
|
| Abstract - #305878 |
|
Title:
|
Why Bayesian Statistics and Machine Learning Need Each Other
|
|
Author(s):
|
Sayan Mukerjee*+
|
|
Companies:
|
Duke University
|
|
Address:
|
112 Old Chemistry, Durham, NC, 27708,
|
|
Keywords:
|
|
|
Abstract:
|
The incorporation of Machine Learning into main stream statistics and the explosion of Bayesian analysis have been two exciting phenomena in modern statistical practice and research. The roundtable will discuss whether there are underlying reasons for these events to occur approximately cotemporally. It will also focus on why these two approaches to statistics with a strong concern for computational issues have historically had fundamental motivational differences, how these communities are intersecting, and complimentarity. Two specific issues that will focus the discussion will be 1) model identifiability versus pure predictive modeling, 2) high-dimensional data and the curse of dimensionality.
|
- 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 2009 program |
