Mutual Information Theory Toolkit with Core Functions for MATLAB

This MATLAB toolbox for mutual information theory provides robust implementations of key information-theoretic measures, including mutual information calculation algorithms, entropy computation methods, and related statistical formulas. The toolkit contains optimized functions that implement mathematical formulations such as Shannon entropy (H(X) = -Σ p(x)log p(x)) and mutual information (I(X;Y) = H(X) + H(Y) - H(X,Y)) using efficient probability estimation techniques. Key features include histogram-based and kernel density estimation methods for probability distribution calculation, with functions designed to handle both discrete and continuous variables. The toolbox enables comprehensive analysis of variable relationships, including feature correlations in datasets and dependency structures between variables. Implementation details incorporate bias correction techniques for small sample sizes and support for various distance metrics in continuous mutual information calculations. This powerful toolkit finds applications across multiple domains including signal processing (for feature selection and system analysis), machine learning (in feature importance ranking and dependency discovery), and data analytics (for pattern recognition and relationship mining). Researchers can leverage the included visualization functions to plot entropy landscapes, mutual information matrices, and dependency graphs, facilitating the discovery of hidden patterns and insights into complex system behaviors through information-theoretic analysis.