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Information Theoretical Estimators 0.63

by szzoli - June 9, 2016, 23:42:14 CET [ ] 315770 views, 59222 downloads, 0 subscriptions

About: ITE (Information Theoretical Estimators) is capable of estimating many different variants of 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.

Changes:

Conditional Shannon entropy estimation: added.
Conditional Shannon mutual information estimation: included.

Authors: Zoltan Szabo
License: Gpl Version 3 Or Later
Programming Language: Python, Matlab, Octave

Operating System: Linux, Windows
Data Formats: Matlab, Octave
JMLR-MLOSS Publication: JMLR Page
Tags: Conditional Entropy, Conditional Mutual Information, Entropy, Mutual Information, Divergence, Independent Subspace Analysis, Separation Principles, Independent Process Analysis, Association Measure, Measure Of Concordance, Measure Of Independence, Nonparametric Estimation, Distribution Regression

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Information Theoretical Estimators 0.63