MATLAB Code Implementation for Calculating Mutual Information

Calculating mutual information is a method used to measure the degree of association between two variables. In information theory, mutual information quantifies the correlation between two random variables - higher values indicate stronger dependence between variables. The calculation process involves detailed mathematical computations requiring knowledge of probability theory and information theory. Although mutual information calculation methods are not uncommon online, few resources provide in-depth mathematical theory alongside practical implementation details. This implementation typically involves probability distribution estimation (using histograms or kernel density estimation), entropy calculations, and joint probability computations. Key MATLAB functions may include histcounts for discretization, unique for value counting, and custom entropy functions using logarithmic operations. The algorithm follows these core steps: 1) Data discretization for continuous variables, 2) Probability distribution estimation, 3) Individual and joint entropy calculation, 4) Mutual information computation using I(X;Y)=H(X)+H(Y)-H(X,Y).