Connectionists: Information Theoretical Estimators Toolbox

[Zoltan Szabo]

Dear Connectionist-ers,

We are pleased to announce our recently released ITE (Information
Theoretical Estimators) toolbox. 

The ITE package could be of interest to many of you: the estimation of
information theoretical quantities (entropy, mutual information,
divergence) plays a central role in numerous important
problems of computational neuroscience.

Unfortunately, available packages focus on (i) discrete variables, or
(ii) quite specialized applications and information theoretical
estimation methods. To fill in this serious gap, we have recently
released ITE (i) a highly modular, (ii) free and open source, (iii)
multi-platform toolbox package, which

1. is capable of estimating many different variants of entropy, mutual
information and divergence measures.

2. offers a simple and unified framework to (a) easily construct new
estimators from existing ones or from scratch, and (b) transparently
use the obtained estimators in information theoretical optimization
problems. 

3. with a prototype application in a central problem family of signal
processing, independent subspace analysis and its extensions.

The homepage of ITE is "https://bitbucket.org/szzoli/ite/". Feel free
to use it.

Best, 

Zoltan ("http://nipg.inf.elte.hu/szzoli")