Non-Parametric Entropy Estimation Toolbox (NPEET). This package contains Python code implementing several entropy estimation functions for both discrete and continuous variables. Information theory provides a model-free way find structure in complex systems, but difficulties in estimating these quantities has traditionally made these techniques infeasible. This package attempts to allay these difficulties by making modern state-of-the-art entropy estimation methods accessible in a single easy-to-use python library. The implementation is very simple. It only requires that numpy/scipy be installed. It includes estimators for entropy, mutual information, and conditional mutual information for both continuous and discrete variables. Additionally it includes a KL Divergence estimator for continuous distributions and mutual information estimator between continuous and discrete variables along with some non-parametric tests for evaluating estimator performance.
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References in zbMATH (referenced in 1 article )
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- Pant, Sanjay; Lombardi, Damiano: An information-theoretic approach to assess practical identifiability of parametric dynamical systems (2015)