Convergence of MDL and Bayesian Methods
author: Tong Zhang,
Department of Statistics, Rutgers, The State University of New Jersey
published: Feb. 25, 2007, recorded: October 2004, views: 4603
published: Feb. 25, 2007, recorded: October 2004, views: 4603
Related content
Report a problem or upload files
If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Description
We introduce a complexity measure which we call KL-complexity. Based on this concept, we present a general information exponential inequality that measures the statistical complexity of some deterministic and randomized estimators. We show that simple and clean finite sample convergence bounds can be obtained from this approach. In particular, we are able to improve some classical results concerning the convergence of MDL density estimation and Bayesian posterior distributions
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Write your own review or comment: