Information Theoretical Estimators (ITE) Toolbox

ITE is can estimate several entropy, mutual information, divergence, association measures, cross quantities and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems.

ITE is
(i) written in Matlab/Octave,
(ii) multi-platform (tested extensively on Windows and Linux),
(iii) free and open source (released under the GNU GPLv3(>=) license).

ITE offers
(i) solvers 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.
(ii) several consistency tests (analytical vs estimated value),
(iii) illustrations for information theoretical image registration and distribution regression [supervised entropy learning and aerosol prediction based on multispectral satellite images].

For further details, see "https://bitbucket.org/szzoli/ite-in-python/" (Python: new), "https://bitbucket.org/szzoli/ite/" (Matlab/Octave)