ITE (Information Theoretical Estimators)
ITE is capable of estimating many different variants of entropy, mutual information, divergence measures, and related cross quantities. 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-, Tsallis 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-, Kullback-Leibler divergence; Hellinger-, Bhattacharyya distance; maximum mean discrepancy, J-distance, and cross-entropy.
ITE offers solution methods for
- Independent Subspace Analysis (ISA) and
- its extensions to different linear-, controlled-, post nonlinear-, complex valued-, partially observed models, as well as to systems with nonparametric source dynamics.
- the evolution of the ITE code is briefly summarized in CHANGELOG.txt.
- become a Follower to be always up-to-date with ITE.
- if you have an entropy, mutual information, divergence estimator/subtask solver with a GPLv3(>=)-compatible license that you would like to be embedded into ITE, feel free to contact me.
Download the latest release: