ITE (Information Theoretical Estimators)
ITE is capable of estimating many different variants of entropy, mutual information and divergence measures. Thanks to its highly modular design, ITE supports additionally
- the combinations of the estimation techniques,
- the easy construction and embedding of novel information theoretical estimators, and
- their immediate application in information theoretical optimization problems.
- written in Matlab/Octave,
- multi-platform (tested extensively on Windows and Linux),
- free and open source (released under the GNU GPLv3(>=) license).
ITE can estimate Shannon-, Rényi entropy; generalized variance, kernel canonical correlation analysis, kernel generalized variance, Hilbert-Schmidt independence criterion, Shannon-, L2-, Rényi-, Tsallis mutual information, copula-based kernel dependency, multivariate version of Hoeffding's Phi, Schweizer-Wolff's sigma and kappa; complex variants of entropy and mutual information; L2-, Rényi-, Tsallis divergence, maximum mean discrepancy, and J-distance.
ITE offers solution methods for
- Independent Subspace Analysis (ISA) and
- its extensions to different linear-, controlled-, post nonlinear-, complex valued-, partially observed systems, as well as to systems with nonparametric source dynamics.