On the Estimation of alpha-Divergences

author: Barnabás Póczos, Machine Learning Department, School of Computer Science, Carnegie Mellon University
published: May 6, 2011,   recorded: April 2011,   views: 3276


Related Open Educational Resources

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.
Lecture popularity: You need to login to cast your vote.


We propose new nonparametric, consistent Renyi-alpha and Tsallis-alpha divergence estimators for continuous distributions. Given two independent and identically distributed samples, a ``naive'' approach would be to simply estimate the underlying densities and plug the estimated densities into the corresponding formulas. Our proposed estimators, in contrast, avoid density estimation completely, estimating the divergences directly using only simple k-nearest-neighbor statistics. We are nonetheless able to prove that the estimators are consistent under certain conditions. We also describe how to apply these estimators to mutual information and demonstrate their efficiency via numerical experiments.

See Also:

Download slides icon Download slides: aistats2011_poczes_estimation_01.pdf (1.6 MB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Reviews and comments:

Comment1 vvio, August 26, 2019 at 4:52 a.m.:


Write your own review or comment:

make sure you have javascript enabled or clear this field: