Speaker |
Chris Oates |
|---|---|
Affiliation |
University of Technology Sydney |
Date |
Friday, 15 April 2016 |
Time |
13:00-14:00 |
Location |
Zoom |
Link |
G08 |
Event series |
DeepMind/ELLIS CSML Seminar Series |
Abstract |
In 1972, Charles Stein published a central limit theorem for correlated variables. The mathematical approach used in the proof has since become known as Stein’s Method. This talk provides an introduction to Stein’s Method and describes a formal generalisation, based on Stein Operators. A characterisation of the action of Stein Operators on Hilbert spaces offers considerable potential for applications in kernel-based machine learning. One such application is presented, in the context of numerical integration for Bayesian posterior computation. |
Biography |