MATLAB Implementation of Various Entropy Measures in Information Theory

This toolkit provides MATLAB implementations for various entropy measures in information theory. The package includes comprehensive functions for calculating self-information (using the formula -log2(p) where p is probability), mutual information (measuring the mutual dependence between variables), conditional entropy (uncertainty remaining after accounting for known variables), joint entropy (total uncertainty of multiple variables), redundancy (shared information), and other fundamental metrics. The implementation features efficient probability estimation methods and vectorized operations for optimal performance. Additionally, the toolkit contains practical application examples demonstrating usage in areas like speech recognition (feature analysis using entropy measures) and image processing (texture analysis through entropy calculations). This resource aims to provide students and researchers in information theory with a convenient, practical toolset that includes detailed function documentation and code comments explaining the mathematical foundations and algorithmic approaches.