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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.

ITE is

  • 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; 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.